Instruction: What field is the article from?

Prompt:
Title: Robust Data Pruning under Label Noise via Maximizing Re-labeling Accuracy
Abstract: Data pruning, which aims to downsize a large training set into a small
informative subset, is crucial for reducing the enormous computational costs of
modern deep learning. Though large-scale data collections invariably contain
annotation noise and numerous robust learning methods have been developed, data
pruning for the noise-robust learning scenario has received little attention.
With state-of-the-art Re-labeling methods that self-correct erroneous labels
while training, it is challenging to identify which subset induces the most
accurate re-labeling of erroneous labels in the entire training set. In this
paper, we formalize the problem of data pruning with re-labeling. We first show
that the likelihood of a training example being correctly re-labeled is
proportional to the prediction confidence of its neighborhood in the subset.
Therefore, we propose a novel data pruning algorithm, Prune4Rel, that finds a
subset maximizing the total neighborhood confidence of all training examples,
thereby maximizing the re-labeling accuracy and generalization performance.
Extensive experiments on four real and one synthetic noisy datasets show that
\algname{} outperforms the baselines with Re-labeling models by up to 9.1% as
well as those with a standard model by up to 21.6%.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: CAMRA: Copilot for AMR Annotation
Abstract: In this paper, we introduce CAMRA (Copilot for AMR Annotatations), a
cutting-edge web-based tool designed for constructing Abstract Meaning
Representation (AMR) from natural language text. CAMRA offers a novel approach
to deep lexical semantics annotation such as AMR, treating AMR annotation akin
to coding in programming languages. Leveraging the familiarity of programming
paradigms, CAMRA encompasses all essential features of existing AMR editors,
including example lookup, while going a step further by integrating Propbank
roleset lookup as an autocomplete feature within the tool. Notably, CAMRA
incorporates AMR parser models as coding co-pilots, greatly enhancing the
efficiency and accuracy of AMR annotators. To demonstrate the tool's
capabilities, we provide a live demo accessible at: https://camra.colorado.edu

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: The Rise of the AI Co-Pilot: Lessons for Design from Aviation and Beyond
Abstract: The fast pace of advances in AI promises to revolutionize various aspects of
knowledge work, extending its influence to daily life and professional fields
alike. We advocate for a paradigm where AI is seen as a collaborative co-pilot,
working under human guidance rather than as a mere tool. Drawing from relevant
research and literature in the disciplines of Human-Computer Interaction and
Human Factors Engineering, we highlight the criticality of maintaining human
oversight in AI interactions. Reflecting on lessons from aviation, we address
the dangers of over-relying on automation, such as diminished human vigilance
and skill erosion. Our paper proposes a design approach that emphasizes active
human engagement, control, and skill enhancement in the AI partnership, aiming
to foster a harmonious, effective, and empowering human-AI relationship. We
particularly call out the critical need to design AI interaction capabilities
and software applications to enable and celebrate the primacy of human agency.
This calls for designs for human-AI partnership that cede ultimate control and
responsibility to the human user as pilot, with the AI co-pilot acting in a
well-defined supporting role.

Output: Human-Computer Interaction


Instruction: What field is the article from?

Prompt:
Title: Data Contamination Quiz: A Tool to Detect and Estimate Contamination in Large Language Models
Abstract: We propose the Data Contamination Quiz, a simple and effective approach to
detect data contamination in large language models (LLMs) and estimate the
amount of it. Specifically, we frame data contamination detection as a series
of multiple-choice questions. We devise a quiz format wherein three perturbed
versions of each dataset instance are created. These changes only include
word-level perturbations, replacing words with their contextual synonyms,
ensuring both the semantic and sentence structure remain exactly the same as
the original instance. Together with the original instance, these perturbed
versions constitute the choices in the quiz. Given that the only distinguishing
signal among these choices is the exact wording, an LLM, when tasked with
identifying the original instance from the choices, opts for the original if it
has memorized it in its pre-training phase--a trait intrinsic to LLMs. A
dataset partition is then marked as contaminated if the LLM's performance on
the quiz surpasses what random chance suggests. Our evaluation spans seven
datasets and their respective splits (train and test/validation) on two
state-of-the-art LLMs: GPT-4 and GPT-3.5. While lacking access to the
pre-training data, our results suggest that our approach not only enhances the
detection of data contamination but also provides an accurate estimation of its
extent, even when the contamination signal is weak.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Multi-criteria recommendation systems to foster online grocery
Abstract: With the exponential increase in information, it has become imperative to
design mechanisms that allow users to access what matters to them as quickly as
possible. The recommendation system ($RS$) with information technology
development is the solution, it is an intelligent system. Various types of data
can be collected on items of interest to users and presented as
recommendations. $RS$ also play a very important role in e-commerce. The
purpose of recommending a product is to designate the most appropriate
designation for a specific product. The major challenges when recommending
products are insufficient information about the products and the categories to
which they belong. In this paper, we transform the product data using two
methods of document representation: bag-of-words (BOW) and the neural
network-based document combination known as vector-based (Doc2Vec). We propose
three-criteria recommendation systems (product, package, and health) for each
document representation method to foster online grocery, which depends on
product characteristics such as (composition, packaging, nutrition table,
allergen, etc.). For our evaluation, we conducted a user and expert survey.
Finally, we have compared the performance of these three criteria for each
document representation method, discovering that the neural network-based
(Doc2Vec) performs better and completely alters the results.

Output: Information Retrieval


Instruction: What field is the article from?

Prompt:
Title: Generalization to New Sequential Decision Making Tasks with In-Context Learning
Abstract: Training autonomous agents that can learn new tasks from only a handful of
demonstrations is a long-standing problem in machine learning. Recently,
transformers have been shown to learn new language or vision tasks without any
weight updates from only a few examples, also referred to as in-context
learning. However, the sequential decision making setting poses additional
challenges having a lower tolerance for errors since the environment's
stochasticity or the agent's actions can lead to unseen, and sometimes
unrecoverable, states. In this paper, we use an illustrative example to show
that naively applying transformers to sequential decision making problems does
not enable in-context learning of new tasks. We then demonstrate how training
on sequences of trajectories with certain distributional properties leads to
in-context learning of new sequential decision making tasks. We investigate
different design choices and find that larger model and dataset sizes, as well
as more task diversity, environment stochasticity, and trajectory burstiness,
all result in better in-context learning of new out-of-distribution tasks. By
training on large diverse offline datasets, our model is able to learn new
MiniHack and Procgen tasks without any weight updates from just a handful of
demonstrations.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Math-Shepherd: A Label-Free Step-by-Step Verifier for LLMs in Mathematical Reasoning
Abstract: Large language models (LLMs) have demonstrated remarkable capabilities across
a wide range of tasks. However, even the most advanced open-source LLMs, such
as the LLaMA family models, still face challenges when it comes to accurately
solving complex multi-step mathematical problems. In this paper, we present an
innovative process-oriented math verifier called \textbf{Math-Shepherd}, which
assigns a reward score to each step of the LLM's outputs on math problems. The
training of Math-Shepherd is achieved using automatically constructed
process-wise supervision data, breaking the bottleneck of heavy reliance on
manual annotation in existing work. With the guidance of Math-Shepherd, a
series of open-source LLMs demonstrate exceptional performance. Among them,
DeepSeek 67B \citep{DeepSeek-llm} stands out by achieving accuracy rates of
93.3\% on the GSM8K dataset and 48.1\% on the MATH dataset, without external
enhancement such as tool usage. Our Math-Shepherd also outperforms the
self-consistency method and other existing verification models. We believe that
automatic process supervision holds significant potential for the future
evolution of LLMs.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Replay across Experiments: A Natural Extension of Off-Policy RL
Abstract: Replaying data is a principal mechanism underlying the stability and data
efficiency of off-policy reinforcement learning (RL). We present an effective
yet simple framework to extend the use of replays across multiple experiments,
minimally adapting the RL workflow for sizeable improvements in controller
performance and research iteration times. At its core, Replay Across
Experiments (RaE) involves reusing experience from previous experiments to
improve exploration and bootstrap learning while reducing required changes to a
minimum in comparison to prior work. We empirically show benefits across a
number of RL algorithms and challenging control domains spanning both
locomotion and manipulation, including hard exploration tasks from egocentric
vision. Through comprehensive ablations, we demonstrate robustness to the
quality and amount of data available and various hyperparameter choices.
Finally, we discuss how our approach can be applied more broadly across
research life cycles and can increase resilience by reloading data across
random seeds or hyperparameter variations.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: End-to-End Autoregressive Retrieval via Bootstrapping for Smart Reply Systems
Abstract: Reply suggestion systems represent a staple component of many instant
messaging and email systems. However, the requirement to produce sets of
replies, rather than individual replies, makes the task poorly suited for
out-of-the-box retrieval architectures, which only consider individual
message-reply similarity. As a result, these system often rely on additional
post-processing modules to diversify the outputs. However, these approaches are
ultimately bottlenecked by the performance of the initial retriever, which in
practice struggles to present a sufficiently diverse range of options to the
downstream diversification module, leading to the suggestions being less
relevant to the user. In this paper, we consider a novel approach that
radically simplifies this pipeline through an autoregressive text-to-text
retrieval model, that learns the smart reply task end-to-end from a dataset of
(message, reply set) pairs obtained via bootstrapping. Empirical results show
this method consistently outperforms a range of state-of-the-art baselines
across three datasets, corresponding to a 5.1%-17.9% improvement in relevance,
and a 0.5%-63.1% improvement in diversity compared to the best baseline
approach. We make our code publicly available.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: NNG-Mix: Improving Semi-supervised Anomaly Detection with Pseudo-anomaly Generation
Abstract: Anomaly detection (AD) is essential in identifying rare and often critical
events in complex systems, finding applications in fields such as network
intrusion detection, financial fraud detection, and fault detection in
infrastructure and industrial systems. While AD is typically treated as an
unsupervised learning task due to the high cost of label annotation, it is more
practical to assume access to a small set of labeled anomaly samples from
domain experts, as is the case for semi-supervised anomaly detection.
Semi-supervised and supervised approaches can leverage such labeled data,
resulting in improved performance. In this paper, rather than proposing a new
semi-supervised or supervised approach for AD, we introduce a novel algorithm
for generating additional pseudo-anomalies on the basis of the limited labeled
anomalies and a large volume of unlabeled data. This serves as an augmentation
to facilitate the detection of new anomalies. Our proposed algorithm, named
Nearest Neighbor Gaussian Mixup (NNG-Mix), efficiently integrates information
from both labeled and unlabeled data to generate pseudo-anomalies. We compare
the performance of this novel algorithm with commonly applied augmentation
techniques, such as Mixup and Cutout. We evaluate NNG-Mix by training various
existing semi-supervised and supervised anomaly detection algorithms on the
original training data along with the generated pseudo-anomalies. Through
extensive experiments on 57 benchmark datasets in ADBench, reflecting different
data types, we demonstrate that NNG-Mix outperforms other data augmentation
methods. It yields significant performance improvements compared to the
baselines trained exclusively on the original training data. Notably, NNG-Mix
yields up to 16.4%, 8.8%, and 8.0% improvements on Classical, CV, and NLP
datasets in ADBench. Our source code will be available at
https://github.com/donghao51/NNG-Mix.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Predicting Ground Reaction Force from Inertial Sensors
Abstract: The study of ground reaction forces (GRF) is used to characterize the
mechanical loading experienced by individuals in movements such as running,
which is clinically applicable to identify athletes at risk for stress-related
injuries. Our aim in this paper is to determine if data collected with inertial
measurement units (IMUs), that can be worn by athletes during outdoor runs, can
be used to predict GRF with sufficient accuracy to allow the analysis of its
derived biomechanical variables (e.g., contact time and loading rate).
  In this paper, we consider lightweight approaches in contrast to
state-of-the-art prediction using LSTM neural networks. Specifically, we
compare use of LSTMs to k-Nearest Neighbors (KNN) regression as well as propose
a novel solution, SVD Embedding Regression (SER), using linear regression
between singular value decomposition embeddings of IMUs data (input) and GRF
data (output). We evaluate the accuracy of these techniques when using training
data collected from different athletes, from the same athlete, or both, and we
explore the use of acceleration and angular velocity data from sensors at
different locations (sacrum and shanks). Our results illustrate that simple
machine learning methods such as SER and KNN can be similarly accurate or more
accurate than LSTM neural networks, with much faster training times and
hyperparameter optimization; in particular, SER and KNN are more accurate when
personal training data are available, and KNN comes with benefit of providing
provenance of prediction. Notably, the use of personal data reduces prediction
errors of all methods for most biomechanical variables.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Causality and Explainability for Trustworthy Integrated Pest Management
Abstract: Pesticides serve as a common tool in agricultural pest control but
significantly contribute to the climate crisis. To combat this, Integrated Pest
Management (IPM) stands as a climate-smart alternative. Despite its potential,
IPM faces low adoption rates due to farmers' skepticism about its
effectiveness. To address this challenge, we introduce an advanced data
analysis framework tailored to enhance IPM adoption. Our framework provides i)
robust pest population predictions across diverse environments with invariant
and causal learning, ii) interpretable pest presence predictions using
transparent models, iii) actionable advice through counterfactual explanations
for in-season IPM interventions, iv) field-specific treatment effect
estimations, and v) assessments of the effectiveness of our advice using causal
inference. By incorporating these features, our framework aims to alleviate
skepticism and encourage wider adoption of IPM practices among farmers.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Multi-modal Latent Space Learning for Chain-of-Thought Reasoning in Language Models
Abstract: Chain-of-thought (CoT) reasoning has exhibited impressive performance in
language models for solving complex tasks and answering questions. However,
many real-world questions require multi-modal information, such as text and
images. Previous research on multi-modal CoT has primarily focused on
extracting fixed image features from off-the-shelf vision models and then
fusing them with text using attention mechanisms. This approach has limitations
because these vision models were not designed for complex reasoning tasks and
do not align well with language thoughts. To overcome this limitation, we
introduce a novel approach for multi-modal CoT reasoning that utilizes latent
space learning via diffusion processes to generate effective image features
that align with language thoughts. Our method fuses image features and text
representations at a deep level and improves the complex reasoning ability of
multi-modal CoT. We demonstrate the efficacy of our proposed method on
multi-modal ScienceQA and machine translation benchmarks, achieving
state-of-the-art performance on ScienceQA. Overall, our approach offers a more
robust and effective solution for multi-modal reasoning in language models,
enhancing their ability to tackle complex real-world problems.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Systematic AI Approach for AGI: Addressing Alignment, Energy, and AGI Grand Challenges
Abstract: AI faces a trifecta of grand challenges the Energy Wall, the Alignment
Problem and the Leap from Narrow AI to AGI. Contemporary AI solutions consume
unsustainable amounts of energy during model training and daily
operations.Making things worse, the amount of computation required to train
each new AI model has been doubling every 2 months since 2020, directly
translating to increases in energy consumption.The leap from AI to AGI requires
multiple functional subsystems operating in a balanced manner, which requires a
system architecture. However, the current approach to artificial intelligence
lacks system design; even though system characteristics play a key role in the
human brain from the way it processes information to how it makes decisions.
Similarly, current alignment and AI ethics approaches largely ignore system
design, yet studies show that the brains system architecture plays a critical
role in healthy moral decisions.In this paper, we argue that system design is
critically important in overcoming all three grand challenges. We posit that
system design is the missing piece in overcoming the grand challenges.We
present a Systematic AI Approach for AGI that utilizes system design principles
for AGI, while providing ways to overcome the energy wall and the alignment
challenges.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Internet of Federated Digital Twins (IoFDT): Connecting Twins Beyond Borders for Society 5.0
Abstract: The concept of digital twin (DT), which enables the creation of a
programmable, digital representation of physical systems, is expected to
revolutionize future industries and will lie at the heart of the vision of a
future smart society, namely, Society 5.0, in which high integration between
cyber (digital) and physical spaces is exploited to bring economic and societal
advancements. However, the success of such a DT-driven Society 5.0 requires a
synergistic convergence of artificial intelligence and networking technologies
into an integrated, programmable system that can coordinate networks of DTs to
effectively deliver diverse Society 5.0 services. Prior works remain restricted
to either qualitative study, simple analysis or software implementations of a
single DT, and thus, they cannot provide the highly synergistic integration of
digital and physical spaces as required by Society 5.0. In contrast, this paper
envisions a novel concept of an Internet of Federated Digital Twins (IoFDT)
that holistically integrates heterogeneous and physically separated DTs
representing different Society 5.0 services within a single framework and
system. For this concept of IoFDT, we first introduce a hierarchical
architecture that integrates federated DTs through horizontal and vertical
interactions, bridging the cyber and physical spaces to unlock new
possibilities. Then, we discuss the challenges of realizing IoFDT, highlighting
the intricacies across communication, computing, and AI-native networks while
also underscoring potential innovative solutions. Subsequently, we elaborate on
the importance of the implementation of a unified IoFDT platform that
integrates all technical components and orchestrates their interactions,
emphasizing the necessity of practical experimental platforms with a focus on
real-world applications in areas like smart mobility.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: FedReverse: Multiparty Reversible Deep Neural Network Watermarking
Abstract: The proliferation of Deep Neural Networks (DNN) in commercial applications is
expanding rapidly. Simultaneously, the increasing complexity and cost of
training DNN models have intensified the urgency surrounding the protection of
intellectual property associated with these trained models. In this regard, DNN
watermarking has emerged as a crucial safeguarding technique. This paper
presents FedReverse, a novel multiparty reversible watermarking approach for
robust copyright protection while minimizing performance impact. Unlike
existing methods, FedReverse enables collaborative watermark embedding from
multiple parties after model training, ensuring individual copyright claims. In
addition, FedReverse is reversible, enabling complete watermark removal with
unanimous client consent. FedReverse demonstrates perfect covering, ensuring
that observations of watermarked content do not reveal any information about
the hidden watermark. Additionally, it showcases resistance against Known
Original Attacks (KOA), making it highly challenging for attackers to forge
watermarks or infer the key. This paper further evaluates FedReverse through
comprehensive simulations involving Multi-layer Perceptron (MLP) and
Convolutional Neural Networks (CNN) trained on the MNIST dataset. The
simulations demonstrate FedReverse's robustness, reversibility, and minimal
impact on model accuracy across varying embedding parameters and multiple
client scenarios.

Output: Cryptography and Security


Instruction: What field is the article from?

Prompt:
Title: Learning Multi-graph Structure for Temporal Knowledge Graph Reasoning
Abstract: Temporal Knowledge Graph (TKG) reasoning that forecasts future events based
on historical snapshots distributed over timestamps is denoted as extrapolation
and has gained significant attention. Owing to its extreme versatility and
variation in spatial and temporal correlations, TKG reasoning presents a
challenging task, demanding efficient capture of concurrent structures and
evolutional interactions among facts. While existing methods have made strides
in this direction, they still fall short of harnessing the diverse forms of
intrinsic expressive semantics of TKGs, which encompass entity correlations
across multiple timestamps and periodicity of temporal information. This
limitation constrains their ability to thoroughly reflect historical
dependencies and future trends. In response to these drawbacks, this paper
proposes an innovative reasoning approach that focuses on Learning Multi-graph
Structure (LMS). Concretely, it comprises three distinct modules concentrating
on multiple aspects of graph structure knowledge within TKGs, including
concurrent and evolutional patterns along timestamps, query-specific
correlations across timestamps, and semantic dependencies of timestamps, which
capture TKG features from various perspectives. Besides, LMS incorporates an
adaptive gate for merging entity representations both along and across
timestamps effectively. Moreover, it integrates timestamp semantics into graph
attention calculations and time-aware decoders, in order to impose temporal
constraints on events and narrow down prediction scopes with historical
statistics. Extensive experimental results on five event-based benchmark
datasets demonstrate that LMS outperforms state-of-the-art extrapolation
models, indicating the superiority of modeling a multi-graph perspective for
TKG reasoning.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Context Shift Reduction for Offline Meta-Reinforcement Learning
Abstract: Offline meta-reinforcement learning (OMRL) utilizes pre-collected offline
datasets to enhance the agent's generalization ability on unseen tasks.
However, the context shift problem arises due to the distribution discrepancy
between the contexts used for training (from the behavior policy) and testing
(from the exploration policy). The context shift problem leads to incorrect
task inference and further deteriorates the generalization ability of the
meta-policy. Existing OMRL methods either overlook this problem or attempt to
mitigate it with additional information. In this paper, we propose a novel
approach called Context Shift Reduction for OMRL (CSRO) to address the context
shift problem with only offline datasets. The key insight of CSRO is to
minimize the influence of policy in context during both the meta-training and
meta-test phases. During meta-training, we design a max-min mutual information
representation learning mechanism to diminish the impact of the behavior policy
on task representation. In the meta-test phase, we introduce the non-prior
context collection strategy to reduce the effect of the exploration policy.
Experimental results demonstrate that CSRO significantly reduces the context
shift and improves the generalization ability, surpassing previous methods
across various challenging domains.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Responsibility in Extensive Form Games
Abstract: Two different forms of responsibility, counterfactual and seeing-to-it, have
been extensively discussed in the philosophy and AI in the context of a single
agent or multiple agents acting simultaneously. Although the generalisation of
counterfactual responsibility to a setting where multiple agents act in some
order is relatively straightforward, the same cannot be said about seeing-to-it
responsibility. Two versions of seeing-to-it modality applicable to such
settings have been proposed in the literature. Neither of them perfectly
captures the intuition of responsibility. This paper proposes a definition of
seeing-to-it responsibility for such settings that amalgamate the two
modalities.
  This paper shows that the newly proposed notion of responsibility and
counterfactual responsibility are not definable through each other and studies
the responsibility gap for these two forms of responsibility. It shows that
although these two forms of responsibility are not enough to ascribe
responsibility in each possible situation, this gap does not exist if
higher-order responsibility is taken into account.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: TPTU-v2: Boosting Task Planning and Tool Usage of Large Language Model-based Agents in Real-world Systems
Abstract: Large Language Models (LLMs) have demonstrated proficiency in addressing
tasks that necessitate a combination of task planning and the usage of external
tools that require a blend of task planning and the utilization of external
tools, such as APIs. However, real-world complex systems present three
prevalent challenges concerning task planning and tool usage: (1) The real
system usually has a vast array of APIs, so it is impossible to feed the
descriptions of all APIs to the prompt of LLMs as the token length is limited;
(2) the real system is designed for handling complex tasks, and the base LLMs
can hardly plan a correct sub-task order and API-calling order for such tasks;
(3) Similar semantics and functionalities among APIs in real systems create
challenges for both LLMs and even humans in distinguishing between them. In
response, this paper introduces a comprehensive framework aimed at enhancing
the Task Planning and Tool Usage (TPTU) abilities of LLM-based agents operating
within real-world systems. Our framework comprises three key components
designed to address these challenges: (1) the API Retriever selects the most
pertinent APIs for the user task among the extensive array available; (2) LLM
Finetuner tunes a base LLM so that the finetuned LLM can be more capable for
task planning and API calling; (3) the Demo Selector adaptively retrieves
different demonstrations related to hard-to-distinguish APIs, which is further
used for in-context learning to boost the final performance. We validate our
methods using a real-world commercial system as well as an open-sourced
academic dataset, and the outcomes clearly showcase the efficacy of each
individual component as well as the integrated framework.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: PIE-NeRF: Physics-based Interactive Elastodynamics with NeRF
Abstract: We show that physics-based simulations can be seamlessly integrated with NeRF
to generate high-quality elastodynamics of real-world objects. Unlike existing
methods, we discretize nonlinear hyperelasticity in a meshless way, obviating
the necessity for intermediate auxiliary shape proxies like a tetrahedral mesh
or voxel grid. A quadratic generalized moving least square (Q-GMLS) is employed
to capture nonlinear dynamics and large deformation on the implicit model. Such
meshless integration enables versatile simulations of complex and codimensional
shapes. We adaptively place the least-square kernels according to the NeRF
density field to significantly reduce the complexity of the nonlinear
simulation. As a result, physically realistic animations can be conveniently
synthesized using our method for a wide range of hyperelastic materials at an
interactive rate. For more information, please visit our project page at
https://fytalon.github.io/pienerf/.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Polynomial-based Self-Attention for Table Representation learning
Abstract: Structured data, which constitutes a significant portion of existing data
types, has been a long-standing research topic in the field of machine
learning. Various representation learning methods for tabular data have been
proposed, ranging from encoder-decoder structures to Transformers. Among these,
Transformer-based methods have achieved state-of-the-art performance not only
in tabular data but also in various other fields, including computer vision and
natural language processing. However, recent studies have revealed that
self-attention, a key component of Transformers, can lead to an oversmoothing
issue. We show that Transformers for tabular data also face this problem, and
to address the problem, we propose a novel matrix polynomial-based
self-attention layer as a substitute for the original self-attention layer,
which enhances model scalability. In our experiments with three representative
table learning models equipped with our proposed layer, we illustrate that the
layer effectively mitigates the oversmoothing problem and enhances the
representation performance of the existing methods, outperforming the
state-of-the-art table representation methods.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Make me an Offer: Forward and Reverse Auctioning Problems in the Tourism Industry
Abstract: Most tourist destinations are facing regular and consistent seasonality with
significant economic and social impacts. This phenomenon is more pronounced in
the post-covid era, where demand for travel has increased but unevenly among
different geographic areas. To counter these problems that both customers and
hoteliers are facing, we have developed two auctioning systems that allow
hoteliers of lower popularity tier areas or during low season periods to
auction their rooms in what we call a forward auction model, and also allows
customers to initiate a bidding process whereby hoteliers in an area may make
offers to the customer for their rooms, in what constitutes a reverse auction
model initiated by the customer, similar to the bidding concept of
priceline.com. We develop mathematical programming models that define
explicitly both types of auctions, and show that in each type, there are
significant benefits to be gained both on the side of the hotelier as well as
on the side of the customer. We discuss algorithmic techniques for the
approximate solution of these optimization problems, and present results using
exact optimization solvers to solve them to guaranteed optimality. These
techniques could be beneficial to both customer and hotelier reducing
seasonality during middle and low season and providing the customer with
attractive offers.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Optimizing the Passenger Flow for Airport Security Check
Abstract: Due to the necessary security for the airport and flight, passengers are
required to have strict security check before getting aboard. However, there
are frequent complaints of wasting huge amount of time while waiting for the
security check. This paper presents a potential solution aimed at optimizing
gate setup procedures specifically tailored for Chicago OHare International
Airport. By referring to queueing theory and performing Monte Carlo
simulations, we propose an approach to significantly diminish the average
waiting time to a more manageable level. Additionally, our study meticulously
examines and identifies the influential factors contributing to this
optimization, providing a comprehensive understanding of their impact.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Modular Control Architecture for Safe Marine Navigation: Reinforcement Learning and Predictive Safety Filters
Abstract: Many autonomous systems face safety challenges, requiring robust closed-loop
control to handle physical limitations and safety constraints. Real-world
systems, like autonomous ships, encounter nonlinear dynamics and environmental
disturbances. Reinforcement learning is increasingly used to adapt to complex
scenarios, but standard frameworks ensuring safety and stability are lacking.
Predictive Safety Filters (PSF) offer a promising solution, ensuring constraint
satisfaction in learning-based control without explicit constraint handling.
This modular approach allows using arbitrary control policies, with the safety
filter optimizing proposed actions to meet physical and safety constraints. We
apply this approach to marine navigation, combining RL with PSF on a simulated
Cybership II model. The RL agent is trained on path following and collision
avpodance, while the PSF monitors and modifies control actions for safety.
Results demonstrate the PSF's effectiveness in maintaining safety without
hindering the RL agent's learning rate and performance, evaluated against a
standard RL agent without PSF.

Output: Robotics


Instruction: What field is the article from?

Prompt:
Title: Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insights
Abstract: Artificial Intelligence (AI) is expected to play an instrumental role in the
next generation of wireless systems, such as sixth-generation (6G) mobile
network. However, massive data, energy consumption, training complexity, and
sensitive data protection in wireless systems are all crucial challenges that
must be addressed for training AI models and gathering intelligence and
knowledge from distributed devices. Federated Learning (FL) is a recent
framework that has emerged as a promising approach for multiple learning agents
to build an accurate and robust machine learning models without sharing raw
data. By allowing mobile handsets and devices to collaboratively learn a global
model without explicit sharing of training data, FL exhibits high privacy and
efficient spectrum utilization. While there are a lot of survey papers
exploring FL paradigms and usability in 6G privacy, none of them has clearly
addressed how FL can be used to improve the protocol stack and wireless
operations. The main goal of this survey is to provide a comprehensive overview
on FL usability to enhance mobile services and enable smart ecosystems to
support novel use-cases. This paper examines the added-value of implementing FL
throughout all levels of the protocol stack. Furthermore, it presents important
FL applications, addresses hot topics, provides valuable insights and explicits
guidance for future research and developments. Our concluding remarks aim to
leverage the synergy between FL and future 6G, while highlighting FL's
potential to revolutionize wireless industry and sustain the development of
cutting-edge mobile services.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Limited Data, Unlimited Potential: A Study on ViTs Augmented by Masked Autoencoders
Abstract: Vision Transformers (ViTs) have become ubiquitous in computer vision. Despite
their success, ViTs lack inductive biases, which can make it difficult to train
them with limited data. To address this challenge, prior studies suggest
training ViTs with self-supervised learning (SSL) and fine-tuning sequentially.
However, we observe that jointly optimizing ViTs for the primary task and a
Self-Supervised Auxiliary Task (SSAT) is surprisingly beneficial when the
amount of training data is limited. We explore the appropriate SSL tasks that
can be optimized alongside the primary task, the training schemes for these
tasks, and the data scale at which they can be most effective. Our findings
reveal that SSAT is a powerful technique that enables ViTs to leverage the
unique characteristics of both the self-supervised and primary tasks, achieving
better performance than typical ViTs pre-training with SSL and fine-tuning
sequentially. Our experiments, conducted on 10 datasets, demonstrate that SSAT
significantly improves ViT performance while reducing carbon footprint. We also
confirm the effectiveness of SSAT in the video domain for deepfake detection,
showcasing its generalizability. Our code is available at
https://github.com/dominickrei/Limited-data-vits.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Automaton Distillation: Neuro-Symbolic Transfer Learning for Deep Reinforcement Learning
Abstract: Reinforcement learning (RL) is a powerful tool for finding optimal policies
in sequential decision processes. However, deep RL methods suffer from two
weaknesses: collecting the amount of agent experience required for practical RL
problems is prohibitively expensive, and the learned policies exhibit poor
generalization on tasks outside of the training distribution. To mitigate these
issues, we introduce automaton distillation, a form of neuro-symbolic transfer
learning in which Q-value estimates from a teacher are distilled into a
low-dimensional representation in the form of an automaton. We then propose two
methods for generating Q-value estimates: static transfer, which reasons over
an abstract Markov Decision Process constructed based on prior knowledge, and
dynamic transfer, where symbolic information is extracted from a teacher Deep
Q-Network (DQN). The resulting Q-value estimates from either method are used to
bootstrap learning in the target environment via a modified DQN loss function.
We list several failure modes of existing automaton-based transfer methods and
demonstrate that both static and dynamic automaton distillation decrease the
time required to find optimal policies for various decision tasks.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: The language of prompting: What linguistic properties make a prompt successful?
Abstract: The latest generation of LLMs can be prompted to achieve impressive zero-shot
or few-shot performance in many NLP tasks. However, since performance is highly
sensitive to the choice of prompts, considerable effort has been devoted to
crowd-sourcing prompts or designing methods for prompt optimisation. Yet, we
still lack a systematic understanding of how linguistic properties of prompts
correlate with task performance. In this work, we investigate how LLMs of
different sizes, pre-trained and instruction-tuned, perform on prompts that are
semantically equivalent, but vary in linguistic structure. We investigate both
grammatical properties such as mood, tense, aspect and modality, as well as
lexico-semantic variation through the use of synonyms. Our findings contradict
the common assumption that LLMs achieve optimal performance on lower perplexity
prompts that reflect language use in pretraining or instruction-tuning data.
Prompts transfer poorly between datasets or models, and performance cannot
generally be explained by perplexity, word frequency, ambiguity or prompt
length. Based on our results, we put forward a proposal for a more robust and
comprehensive evaluation standard for prompting research.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Knowledge Plugins: Enhancing Large Language Models for Domain-Specific Recommendations
Abstract: The significant progress of large language models (LLMs) provides a promising
opportunity to build human-like systems for various practical applications.
However, when applied to specific task domains, an LLM pre-trained on a
general-purpose corpus may exhibit a deficit or inadequacy in two types of
domain-specific knowledge. One is a comprehensive set of domain data that is
typically large-scale and continuously evolving. The other is specific working
patterns of this domain reflected in the data. The absence or inadequacy of
such knowledge impacts the performance of the LLM. In this paper, we propose a
general paradigm that augments LLMs with DOmain-specific KnowledgE to enhance
their performance on practical applications, namely DOKE. This paradigm relies
on a domain knowledge extractor, working in three steps: 1) preparing effective
knowledge for the task; 2) selecting the knowledge for each specific sample;
and 3) expressing the knowledge in an LLM-understandable way. Then, the
extracted knowledge is incorporated through prompts, without any computational
cost of model fine-tuning. We instantiate the general paradigm on a widespread
application, i.e. recommender systems, where critical item attributes and
collaborative filtering signals are incorporated. Experimental results
demonstrate that DOKE can substantially improve the performance of LLMs in
specific domains.

Output: Information Retrieval


Instruction: What field is the article from?

Prompt:
Title: Understanding Parameter Saliency via Extreme Value Theory
Abstract: Deep neural networks are being increasingly implemented throughout society in
recent years. It is useful to identify which parameters trigger
misclassification in diagnosing undesirable model behaviors. The concept of
parameter saliency is proposed and used to diagnose convolutional neural
networks (CNNs) by ranking convolution filters that may have caused
misclassification on the basis of parameter saliency. It is also shown that
fine-tuning the top ranking salient filters efficiently corrects
misidentification on ImageNet. However, there is still a knowledge gap in terms
of understanding why parameter saliency ranking can find the filters inducing
misidentification. In this work, we attempt to bridge the gap by analyzing
parameter saliency ranking from a statistical viewpoint, namely, extreme value
theory. We first show that the existing work implicitly assumes that the
gradient norm computed for each filter follows a normal distribution. Then, we
clarify the relationship between parameter saliency and the score based on the
peaks-over-threshold (POT) method, which is often used to model extreme values.
Finally, we reformulate parameter saliency in terms of the POT method, where
this reformulation is regarded as statistical anomaly detection and does not
require the implicit assumptions of the existing parameter-saliency
formulation. Our experimental results demonstrate that our reformulation can
detect malicious filters as well. Furthermore, we show that the existing
parameter saliency method exhibits a bias against the depth of layers in deep
neural networks. In particular, this bias has the potential to inhibit the
discovery of filters that cause misidentification in situations where domain
shift occurs. In contrast, parameter saliency based on POT shows less of this
bias.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: To Tell The Truth: Language of Deception and Language Models
Abstract: Text-based misinformation permeates online discourses, yet evidence of
people's ability to discern truth from such deceptive textual content is
scarce. We analyze a novel TV game show data where conversations in a
high-stake environment between individuals with conflicting objectives result
in lies. We investigate the manifestation of potentially verifiable language
cues of deception in the presence of objective truth, a distinguishing feature
absent in previous text-based deception datasets. We show that there exists a
class of detectors (algorithms) that have similar truth detection performance
compared to human subjects, even when the former accesses only the language
cues while the latter engages in conversations with complete access to all
potential sources of cues (language and audio-visual). Our model, built on a
large language model, employs a bottleneck framework to learn discernible cues
to determine truth, an act of reasoning in which human subjects often perform
poorly, even with incentives. Our model detects novel but accurate language
cues in many cases where humans failed to detect deception, opening up the
possibility of humans collaborating with algorithms and ameliorating their
ability to detect the truth.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Towards Generic Anomaly Detection and Understanding: Large-scale Visual-linguistic Model (GPT-4V) Takes the Lead
Abstract: Anomaly detection is a crucial task across different domains and data types.
However, existing anomaly detection models are often designed for specific
domains and modalities. This study explores the use of GPT-4V(ision), a
powerful visual-linguistic model, to address anomaly detection tasks in a
generic manner. We investigate the application of GPT-4V in multi-modality,
multi-domain anomaly detection tasks, including image, video, point cloud, and
time series data, across multiple application areas, such as industrial,
medical, logical, video, 3D anomaly detection, and localization tasks. To
enhance GPT-4V's performance, we incorporate different kinds of additional cues
such as class information, human expertise, and reference images as
prompts.Based on our experiments, GPT-4V proves to be highly effective in
detecting and explaining global and fine-grained semantic patterns in
zero/one-shot anomaly detection. This enables accurate differentiation between
normal and abnormal instances. Although we conducted extensive evaluations in
this study, there is still room for future evaluation to further exploit
GPT-4V's generic anomaly detection capacity from different aspects. These
include exploring quantitative metrics, expanding evaluation benchmarks,
incorporating multi-round interactions, and incorporating human feedback loops.
Nevertheless, GPT-4V exhibits promising performance in generic anomaly
detection and understanding, thus opening up a new avenue for anomaly
detection.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Continual Learning with Low Rank Adaptation
Abstract: Recent work using pretrained transformers has shown impressive performance
when fine-tuned with data from the downstream problem of interest. However,
they struggle to retain that performance when the data characteristics changes.
In this paper, we focus on continual learning, where a pre-trained transformer
is updated to perform well on new data, while retaining its performance on data
it was previously trained on. Earlier works have tackled this primarily through
methods inspired from prompt tuning. We question this choice, and investigate
the applicability of Low Rank Adaptation (LoRA) to continual learning. On a
range of domain-incremental learning benchmarks, our LoRA-based solution,
CoLoR, yields state-of-the-art performance, while still being as parameter
efficient as the prompt tuning based methods.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: SmartMask: Context Aware High-Fidelity Mask Generation for Fine-grained Object Insertion and Layout Control
Abstract: The field of generative image inpainting and object insertion has made
significant progress with the recent advent of latent diffusion models.
Utilizing a precise object mask can greatly enhance these applications.
However, due to the challenges users encounter in creating high-fidelity masks,
there is a tendency for these methods to rely on more coarse masks (e.g.,
bounding box) for these applications. This results in limited control and
compromised background content preservation. To overcome these limitations, we
introduce SmartMask, which allows any novice user to create detailed masks for
precise object insertion. Combined with a ControlNet-Inpaint model, our
experiments demonstrate that SmartMask achieves superior object insertion
quality, preserving the background content more effectively than previous
methods. Notably, unlike prior works the proposed approach can also be used
even without user-mask guidance, which allows it to perform mask-free object
insertion at diverse positions and scales. Furthermore, we find that when used
iteratively with a novel instruction-tuning based planning model, SmartMask can
be used to design detailed layouts from scratch. As compared with user-scribble
based layout design, we observe that SmartMask allows for better quality
outputs with layout-to-image generation methods. Project page is available at
https://smartmask-gen.github.io

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Jailbreaking GPT-4V via Self-Adversarial Attacks with System Prompts
Abstract: Existing work on jailbreak Multimodal Large Language Models (MLLMs) has
focused primarily on adversarial examples in model inputs, with less attention
to vulnerabilities in model APIs. To fill the research gap, we carry out the
following work: 1) We discover a system prompt leakage vulnerability in GPT-4V.
Through carefully designed dialogue, we successfully steal the internal system
prompts of GPT-4V. This finding indicates potential exploitable security risks
in MLLMs; 2)Based on the acquired system prompts, we propose a novel MLLM
jailbreaking attack method termed SASP (Self-Adversarial Attack via System
Prompt). By employing GPT-4 as a red teaming tool against itself, we aim to
search for potential jailbreak prompts leveraging stolen system prompts.
Furthermore, in pursuit of better performance, we also add human modification
based on GPT-4's analysis, which further improves the attack success rate to
98.7\%; 3) We evaluated the effect of modifying system prompts to defend
against jailbreaking attacks. Results show that appropriately designed system
prompts can significantly reduce jailbreak success rates. Overall, our work
provides new insights into enhancing MLLM security, demonstrating the important
role of system prompts in jailbreaking, which could be leveraged to greatly
facilitate jailbreak success rates while also holding the potential for
defending against jailbreaks.

Output: Cryptography and Security


Instruction: What field is the article from?

Prompt:
Title: Transformers Learn Higher-Order Optimization Methods for In-Context Learning: A Study with Linear Models
Abstract: Transformers are remarkably good at in-context learning (ICL) -- learning
from demonstrations without parameter updates -- but how they perform ICL
remains a mystery. Recent work suggests that Transformers may learn in-context
by internally running Gradient Descent, a first-order optimization method. In
this paper, we instead demonstrate that Transformers learn to implement
higher-order optimization methods to perform ICL. Focusing on in-context linear
regression, we show that Transformers learn to implement an algorithm very
similar to Iterative Newton's Method, a higher-order optimization method,
rather than Gradient Descent. Empirically, we show that predictions from
successive Transformer layers closely match different iterations of Newton's
Method linearly, with each middle layer roughly computing 3 iterations. In
contrast, exponentially more Gradient Descent steps are needed to match an
additional Transformers layer; this suggests that Transformers have an
comparable rate of convergence with high-order methods such as Iterative
Newton, which are exponentially faster than Gradient Descent. We also show that
Transformers can learn in-context on ill-conditioned data, a setting where
Gradient Descent struggles but Iterative Newton succeeds. Finally, we show
theoretical results which support our empirical findings and have a close
correspondence with them: we prove that Transformers can implement $k$
iterations of Newton's method with $\mathcal{O}(k)$ layers.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Explainable AI in Grassland Monitoring: Enhancing Model Performance and Domain Adaptability
Abstract: Grasslands are known for their high biodiversity and ability to provide
multiple ecosystem services. Challenges in automating the identification of
indicator plants are key obstacles to large-scale grassland monitoring. These
challenges stem from the scarcity of extensive datasets, the distributional
shifts between generic and grassland-specific datasets, and the inherent
opacity of deep learning models. This paper delves into the latter two
challenges, with a specific focus on transfer learning and eXplainable
Artificial Intelligence (XAI) approaches to grassland monitoring, highlighting
the novelty of XAI in this domain. We analyze various transfer learning methods
to bridge the distributional gaps between generic and grassland-specific
datasets. Additionally, we showcase how explainable AI techniques can unveil
the model's domain adaptation capabilities, employing quantitative assessments
to evaluate the model's proficiency in accurately centering relevant input
features around the object of interest. This research contributes valuable
insights for enhancing model performance through transfer learning and
measuring domain adaptability with explainable AI, showing significant promise
for broader applications within the agricultural community.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Exploring Machine Learning Models for Federated Learning: A Review of Approaches, Performance, and Limitations
Abstract: In the growing world of artificial intelligence, federated learning is a
distributed learning framework enhanced to preserve the privacy of individuals'
data. Federated learning lays the groundwork for collaborative research in
areas where the data is sensitive. Federated learning has several implications
for real-world problems. In times of crisis, when real-time decision-making is
critical, federated learning allows multiple entities to work collectively
without sharing sensitive data. This distributed approach enables us to
leverage information from multiple sources and gain more diverse insights. This
paper is a systematic review of the literature on privacy-preserving machine
learning in the last few years based on the Preferred Reporting Items for
Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Specifically, we have
presented an extensive review of supervised/unsupervised machine learning
algorithms, ensemble methods, meta-heuristic approaches, blockchain technology,
and reinforcement learning used in the framework of federated learning, in
addition to an overview of federated learning applications. This paper reviews
the literature on the components of federated learning and its applications in
the last few years. The main purpose of this work is to provide researchers and
practitioners with a comprehensive overview of federated learning from the
machine learning point of view. A discussion of some open problems and future
research directions in federated learning is also provided.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: DeSIQ: Towards an Unbiased, Challenging Benchmark for Social Intelligence Understanding
Abstract: Social intelligence is essential for understanding and reasoning about human
expressions, intents and interactions. One representative benchmark for its
study is Social Intelligence Queries (Social-IQ), a dataset of multiple-choice
questions on videos of complex social interactions. We define a comprehensive
methodology to study the soundness of Social-IQ, as the soundness of such
benchmark datasets is crucial to the investigation of the underlying research
problem. Our analysis reveals that Social-IQ contains substantial biases, which
can be exploited by a moderately strong language model to learn spurious
correlations to achieve perfect performance without being given the context or
even the question. We introduce DeSIQ, a new challenging dataset, constructed
by applying simple perturbations to Social-IQ. Our empirical analysis shows
DeSIQ significantly reduces the biases in the original Social-IQ dataset.
Furthermore, we examine and shed light on the effect of model size, model
style, learning settings, commonsense knowledge, and multi-modality on the new
benchmark performance. Our new dataset, observations and findings open up
important research questions for the study of social intelligence.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: MASP: Scalable GNN-based Planning for Multi-Agent Navigation
Abstract: We investigate the problem of decentralized multi-agent navigation tasks,
where multiple agents need to reach initially unassigned targets in a limited
time. Classical planning-based methods suffer from expensive computation
overhead at each step and offer limited expressiveness for complex cooperation
strategies. In contrast, reinforcement learning (RL) has recently become a
popular paradigm for addressing this issue. However, RL struggles with low data
efficiency and cooperation when directly exploring (nearly) optimal policies in
the large search space, especially with an increased agent number (e.g., 10+
agents) or in complex environments (e.g., 3D simulators). In this paper, we
propose Multi-Agent Scalable GNN-based P lanner (MASP), a goal-conditioned
hierarchical planner for navigation tasks with a substantial number of agents.
MASP adopts a hierarchical framework to divide a large search space into
multiple smaller spaces, thereby reducing the space complexity and accelerating
training convergence. We also leverage graph neural networks (GNN) to model the
interaction between agents and goals, improving goal achievement. Besides, to
enhance generalization capabilities in scenarios with unseen team sizes, we
divide agents into multiple groups, each with a previously trained number of
agents. The results demonstrate that MASP outperforms classical planning-based
competitors and RL baselines, achieving a nearly 100% success rate with minimal
training data in both multi-agent particle environments (MPE) with 50 agents
and a quadrotor 3-dimensional environment (OmniDrones) with 20 agents.
Furthermore, the learned policy showcases zero-shot generalization across
unseen team sizes.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Adinkra Symbol Recognition using Classical Machine Learning and Deep Learning
Abstract: Artificial intelligence (AI) has emerged as a transformative influence,
engendering paradigm shifts in global societies, spanning academia and
industry. However, in light of these rapid advances, addressing the
underrepresentation of black communities and African countries in AI is
crucial. Boosting enthusiasm for AI can be effectively accomplished by
showcasing straightforward applications around tasks like identifying and
categorizing traditional symbols, such as Adinkra symbols, or familiar objects
within the community. In this research endeavor, we dived into classical
machine learning and harnessed the power of deep learning models to tackle the
intricate task of classifying and recognizing Adinkra symbols. The idea led to
a newly constructed ADINKRA dataset comprising 174,338 images meticulously
organized into 62 distinct classes, each representing a singular and emblematic
symbol. We constructed a CNN model for classification and recognition using six
convolutional layers, three fully connected (FC) layers, and optional dropout
regularization. The model is a simpler and smaller version of VGG, with fewer
layers, smaller channel sizes, and a fixed kernel size. Additionally, we tap
into the transfer learning capabilities provided by pre-trained models like VGG
and ResNet. These models assist us in both classifying images and extracting
features that can be used with classical machine learning models. We assess the
model's performance by measuring its accuracy and convergence rate and
visualizing the areas that significantly influence its predictions. These
evaluations serve as a foundational benchmark for future assessments of the
ADINKRA dataset. We hope this application exemplar inspires ideas on the
various uses of AI in organizing our traditional and modern lives.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: PromptBench: A Unified Library for Evaluation of Large Language Models
Abstract: The evaluation of large language models (LLMs) is crucial to assess their
performance and mitigate potential security risks. In this paper, we introduce
PromptBench, a unified library to evaluate LLMs. It consists of several key
components that are easily used and extended by researchers: prompt
construction, prompt engineering, dataset and model loading, adversarial prompt
attack, dynamic evaluation protocols, and analysis tools. PromptBench is
designed to be an open, general, and flexible codebase for research purposes
that can facilitate original study in creating new benchmarks, deploying
downstream applications, and designing new evaluation protocols. The code is
available at: https://github.com/microsoft/promptbench and will be continuously
supported.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: A Survey on Knowledge Editing of Neural Networks
Abstract: Deep neural networks are becoming increasingly pervasive in academia and
industry, matching and surpassing human performance on a wide variety of fields
and related tasks. However, just as humans, even the largest artificial neural
networks make mistakes, and once-correct predictions can become invalid as the
world progresses in time. Augmenting datasets with samples that account for
mistakes or up-to-date information has become a common workaround in practical
applications. However, the well-known phenomenon of catastrophic forgetting
poses a challenge in achieving precise changes in the implicitly memorized
knowledge of neural network parameters, often requiring a full model
re-training to achieve desired behaviors. That is expensive, unreliable, and
incompatible with the current trend of large self-supervised pre-training,
making it necessary to find more efficient and effective methods for adapting
neural network models to changing data. To address this need, knowledge editing
is emerging as a novel area of research that aims to enable reliable,
data-efficient, and fast changes to a pre-trained target model, without
affecting model behaviors on previously learned tasks. In this survey, we
provide a brief review of this recent artificial intelligence field of
research. We first introduce the problem of editing neural networks, formalize
it in a common framework and differentiate it from more notorious branches of
research such as continuous learning. Next, we provide a review of the most
relevant knowledge editing approaches and datasets proposed so far, grouping
works under four different families: regularization techniques, meta-learning,
direct model editing, and architectural strategies. Finally, we outline some
intersections with other fields of research and potential directions for future
works.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: ZeST-NeRF: Using temporal aggregation for Zero-Shot Temporal NeRFs
Abstract: In the field of media production, video editing techniques play a pivotal
role. Recent approaches have had great success at performing novel view image
synthesis of static scenes. But adding temporal information adds an extra layer
of complexity. Previous models have focused on implicitly representing static
and dynamic scenes using NeRF. These models achieve impressive results but are
costly at training and inference time. They overfit an MLP to describe the
scene implicitly as a function of position. This paper proposes ZeST-NeRF, a
new approach that can produce temporal NeRFs for new scenes without retraining.
We can accurately reconstruct novel views using multi-view synthesis techniques
and scene flow-field estimation, trained only with unrelated scenes. We
demonstrate how existing state-of-the-art approaches from a range of fields
cannot adequately solve this new task and demonstrate the efficacy of our
solution. The resulting network improves quantitatively by 15% and produces
significantly better visual results.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Mutual Enhancement of Large and Small Language Models with Cross-Silo Knowledge Transfer
Abstract: While large language models (LLMs) are empowered with broad knowledge, their
task-specific performance is often suboptimal. It necessitates fine-tuning LLMs
with task-specific data, but such data may be inaccessible due to privacy
concerns. In this paper, we propose a novel approach to enhance LLMs with
smaller language models (SLMs) that are trained on clients using their private
task-specific data. To enable mutual enhancement between LLMs and SLMs, we
propose CrossLM, where the SLMs promote the LLM to generate task-specific
high-quality data, and both the LLM and SLMs are enhanced with the generated
data. We evaluate CrossLM using publicly accessible language models across a
range of benchmark tasks. The results demonstrate that CrossLM significantly
enhances the task-specific performance of SLMs on clients and the LLM on the
cloud server simultaneously while preserving the LLM's generalization
capability.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Meta-learning of semi-supervised learning from tasks with heterogeneous attribute spaces
Abstract: We propose a meta-learning method for semi-supervised learning that learns
from multiple tasks with heterogeneous attribute spaces. The existing
semi-supervised meta-learning methods assume that all tasks share the same
attribute space, which prevents us from learning with a wide variety of tasks.
With the proposed method, the expected test performance on tasks with a small
amount of labeled data is improved with unlabeled data as well as data in
various tasks, where the attribute spaces are different among tasks. The
proposed method embeds labeled and unlabeled data simultaneously in a
task-specific space using a neural network, and the unlabeled data's labels are
estimated by adapting classification or regression models in the embedding
space. For the neural network, we develop variable-feature self-attention
layers, which enable us to find embeddings of data with different attribute
spaces with a single neural network by considering interactions among examples,
attributes, and labels. Our experiments on classification and regression
datasets with heterogeneous attribute spaces demonstrate that our proposed
method outperforms the existing meta-learning and semi-supervised learning
methods.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Uncertainty Wrapper in the medical domain: Establishing transparent uncertainty quantification for opaque machine learning models in practice
Abstract: When systems use data-based models that are based on machine learning (ML),
errors in their results cannot be ruled out. This is particularly critical if
it remains unclear to the user how these models arrived at their decisions and
if errors can have safety-relevant consequences, as is often the case in the
medical field. In such cases, the use of dependable methods to quantify the
uncertainty remaining in a result allows the user to make an informed decision
about further usage and draw possible conclusions based on a given result. This
paper demonstrates the applicability and practical utility of the Uncertainty
Wrapper using flow cytometry as an application from the medical field that can
benefit from the use of ML models in conjunction with dependable and
transparent uncertainty quantification.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: 1D-Convolutional transformer for Parkinson disease diagnosis from gait
Abstract: This paper presents an efficient deep neural network model for diagnosing
Parkinson's disease from gait. More specifically, we introduce a hybrid
ConvNet-Transformer architecture to accurately diagnose the disease by
detecting the severity stage. The proposed architecture exploits the strengths
of both Convolutional Neural Networks and Transformers in a single end-to-end
model, where the former is able to extract relevant local features from
Vertical Ground Reaction Force (VGRF) signal, while the latter allows to
capture long-term spatio-temporal dependencies in data. In this manner, our
hybrid architecture achieves an improved performance compared to using either
models individually. Our experimental results show that our approach is
effective for detecting the different stages of Parkinson's disease from gait
data, with a final accuracy of 88%, outperforming other state-of-the-art AI
methods on the Physionet gait dataset. Moreover, our method can be generalized
and adapted for other classification problems to jointly address the feature
relevance and spatio-temporal dependency problems in 1D signals. Our source
code and pre-trained models are publicly available at
https://github.com/SafwenNaimi/1D-Convolutional-transformer-for-Parkinson-disease-diagnosis-from-gait.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Outlier Dimensions Encode Task-Specific Knowledge
Abstract: Representations from large language models (LLMs) are known to be dominated
by a small subset of dimensions with exceedingly high variance. Previous works
have argued that although ablating these outlier dimensions in LLM
representations hurts downstream performance, outlier dimensions are
detrimental to the representational quality of embeddings. In this study, we
investigate how fine-tuning impacts outlier dimensions and show that 1) outlier
dimensions that occur in pre-training persist in fine-tuned models and 2) a
single outlier dimension can complete downstream tasks with a minimal error
rate. Our results suggest that outlier dimensions can encode crucial
task-specific knowledge and that the value of a representation in a single
outlier dimension drives downstream model decisions.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: United We Stand, Divided We Fall: UnityGraph for Unsupervised Procedure Learning from Videos
Abstract: Given multiple videos of the same task, procedure learning addresses
identifying the key-steps and determining their order to perform the task. For
this purpose, existing approaches use the signal generated from a pair of
videos. This makes key-steps discovery challenging as the algorithms lack
inter-videos perspective. Instead, we propose an unsupervised Graph-based
Procedure Learning (GPL) framework. GPL consists of the novel UnityGraph that
represents all the videos of a task as a graph to obtain both intra-video and
inter-videos context. Further, to obtain similar embeddings for the same
key-steps, the embeddings of UnityGraph are updated in an unsupervised manner
using the Node2Vec algorithm. Finally, to identify the key-steps, we cluster
the embeddings using KMeans. We test GPL on benchmark ProceL, CrossTask, and
EgoProceL datasets and achieve an average improvement of 2% on third-person
datasets and 3.6% on EgoProceL over the state-of-the-art.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: How Well Do Feature-Additive Explainers Explain Feature-Additive Predictors?
Abstract: Surging interest in deep learning from high-stakes domains has precipitated
concern over the inscrutable nature of black box neural networks. Explainable
AI (XAI) research has led to an abundance of explanation algorithms for these
black boxes. Such post hoc explainers produce human-comprehensible
explanations, however, their fidelity with respect to the model is not well
understood - explanation evaluation remains one of the most challenging issues
in XAI. In this paper, we ask a targeted but important question: can popular
feature-additive explainers (e.g., LIME, SHAP, SHAPR, MAPLE, and PDP) explain
feature-additive predictors? Herein, we evaluate such explainers on ground
truth that is analytically derived from the additive structure of a model. We
demonstrate the efficacy of our approach in understanding these explainers
applied to symbolic expressions, neural networks, and generalized additive
models on thousands of synthetic and several real-world tasks. Our results
suggest that all explainers eventually fail to correctly attribute the
importance of features, especially when a decision-making process involves
feature interactions.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Context-aware explainable recommendations over knowledge graphs
Abstract: Knowledge graphs contain rich semantic relationships related to items and
incorporating such semantic relationships into recommender systems helps to
explore the latent connections of items, thus improving the accuracy of
prediction and enhancing the explainability of recommendations. However, such
explainability is not adapted to users' contexts, which can significantly
influence their preferences. In this work, we propose CA-KGCN (Context-Aware
Knowledge Graph Convolutional Network), an end-to-end framework that can model
users' preferences adapted to their contexts and can incorporate rich semantic
relationships in the knowledge graph related to items. This framework captures
users' attention to different factors: contexts and features of items. More
specifically, the framework can model users' preferences adapted to their
contexts and provide explanations adapted to the given context. Experiments on
three real-world datasets show the effectiveness of our framework: modeling
users' preferences adapted to their contexts and explaining the recommendations
generated.

Output: Information Retrieval


Instruction: What field is the article from?

Prompt:
Title: Soil Organic Carbon Estimation from Climate-related Features with Graph Neural Network
Abstract: Soil organic carbon (SOC) plays a pivotal role in the global carbon cycle,
impacting climate dynamics and necessitating accurate estimation for
sustainable land and agricultural management. While traditional methods of SOC
estimation face resolution and accuracy challenges, recent technological
solutions harness remote sensing, machine learning, and high-resolution
satellite mapping. Graph Neural Networks (GNNs), especially when integrated
with positional encoders, can capture complex relationships between soil and
climate. Using the LUCAS database, this study compared four GNN operators in
the positional encoder framework. Results revealed that the PESAGE and
PETransformer models outperformed others in SOC estimation, indicating their
potential in capturing the complex relationship between SOC and climate
features. Our findings confirm the feasibility of applications of GNN
architectures in SOC prediction, establishing a framework for future
explorations of this topic with more advanced GNN models.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Training Dynamics of Contextual N-Grams in Language Models
Abstract: Prior work has shown the existence of contextual neurons in language models,
including a neuron that activates on German text. We show that this neuron
exists within a broader contextual n-gram circuit: we find late layer neurons
which recognize and continue n-grams common in German text, but which only
activate if the German neuron is active. We investigate the formation of this
circuit throughout training and find that it is an example of what we call a
second-order circuit. In particular, both the constituent n-gram circuits and
the German detection circuit which culminates in the German neuron form with
independent functions early in training - the German detection circuit
partially through modeling German unigram statistics, and the n-grams by
boosting appropriate completions. Only after both circuits have already formed
do they fit together into a second-order circuit. Contrary to the hypotheses
presented in prior work, we find that the contextual n-gram circuit forms
gradually rather than in a sudden phase transition. We further present a range
of anomalous observations such as a simultaneous phase transition in many tasks
coinciding with the learning rate warm-up, and evidence that many context
neurons form simultaneously early in training but are later unlearned.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Ontology Learning Using Formal Concept Analysis and WordNet
Abstract: Manual ontology construction takes time, resources, and domain specialists.
Supporting a component of this process for automation or semi-automation would
be good. This project and dissertation provide a Formal Concept Analysis and
WordNet framework for learning concept hierarchies from free texts. The process
has steps. First, the document is Part-Of-Speech labeled, then parsed to
produce sentence parse trees. Verb/noun dependencies are derived from parse
trees next. After lemmatizing, pruning, and filtering the word pairings, the
formal context is created. The formal context may contain some erroneous and
uninteresting pairs because the parser output may be erroneous, not all derived
pairs are interesting, and it may be large due to constructing it from a large
free text corpus. Deriving lattice from the formal context may take longer,
depending on the size and complexity of the data. Thus, decreasing formal
context may eliminate erroneous and uninteresting pairs and speed up idea
lattice derivation. WordNet-based and Frequency-based approaches are tested.
Finally, we compute formal idea lattice and create a classical concept
hierarchy. The reduced concept lattice is compared to the original to evaluate
the outcomes. Despite several system constraints and component discrepancies
that may prevent logical conclusion, the following data imply idea hierarchies
in this project and dissertation are promising. First, the reduced idea lattice
and original concept have commonalities. Second, alternative language or
statistical methods can reduce formal context size. Finally, WordNet-based and
Frequency-based approaches reduce formal context differently, and the order of
applying them is examined to reduce context efficiently.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Course Correcting Koopman Representations
Abstract: Koopman representations aim to learn features of nonlinear dynamical systems
(NLDS) which lead to linear dynamics in the latent space. Theoretically, such
features can be used to simplify many problems in modeling and control of NLDS.
In this work we study autoencoder formulations of this problem, and different
ways they can be used to model dynamics, specifically for future state
prediction over long horizons. We discover several limitations of predicting
future states in the latent space and propose an inference-time mechanism,
which we refer to as Periodic Reencoding, for faithfully capturing long term
dynamics. We justify this method both analytically and empirically via
experiments in low and high dimensional NLDS.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: A Survey of Adversarial CAPTCHAs on its History, Classification and Generation
Abstract: Completely Automated Public Turing test to tell Computers and Humans Apart,
short for CAPTCHA, is an essential and relatively easy way to defend against
malicious attacks implemented by bots. The security and usability trade-off
limits the use of massive geometric transformations to interfere deep model
recognition and deep models even outperformed humans in complex CAPTCHAs. The
discovery of adversarial examples provides an ideal solution to the security
and usability trade-off by integrating adversarial examples and CAPTCHAs to
generate adversarial CAPTCHAs that can fool the deep models. In this paper, we
extend the definition of adversarial CAPTCHAs and propose a classification
method for adversarial CAPTCHAs. Then we systematically review some commonly
used methods to generate adversarial examples and methods that are successfully
used to generate adversarial CAPTCHAs. Also, we analyze some defense methods
that can be used to defend adversarial CAPTCHAs, indicating potential threats
to adversarial CAPTCHAs. Finally, we discuss some possible future research
directions for adversarial CAPTCHAs at the end of this paper.

Output: Cryptography and Security


Instruction: What field is the article from?

Prompt:
Title: Safety-aware Causal Representation for Trustworthy Reinforcement Learning in Autonomous Driving
Abstract: In the domain of autonomous driving, the Learning from Demonstration (LfD)
paradigm has exhibited notable efficacy in addressing sequential
decision-making problems. However, consistently achieving safety in varying
traffic contexts, especially in safety-critical scenarios, poses a significant
challenge due to the long-tailed and unforeseen scenarios absent from offline
datasets. In this paper, we introduce the saFety-aware strUctured Scenario
representatION (FUSION), a pioneering methodology conceived to facilitate the
learning of an adaptive end-to-end driving policy by leveraging structured
scenario information. FUSION capitalizes on the causal relationships between
decomposed reward, cost, state, and action space, constructing a framework for
structured sequential reasoning under dynamic traffic environments. We conduct
rigorous evaluations in two typical real-world settings of distribution shift
in autonomous vehicles, demonstrating the good balance between safety cost and
utility reward of FUSION compared to contemporary state-of-the-art safety-aware
LfD baselines. Empirical evidence under diverse driving scenarios attests that
FUSION significantly enhances the safety and generalizability of autonomous
driving agents, even in the face of challenging and unseen environments.
Furthermore, our ablation studies reveal noticeable improvements in the
integration of causal representation into the safe offline RL problem.

Output: Robotics


Instruction: What field is the article from?

Prompt:
Title: Emu Edit: Precise Image Editing via Recognition and Generation Tasks
Abstract: Instruction-based image editing holds immense potential for a variety of
applications, as it enables users to perform any editing operation using a
natural language instruction. However, current models in this domain often
struggle with accurately executing user instructions. We present Emu Edit, a
multi-task image editing model which sets state-of-the-art results in
instruction-based image editing. To develop Emu Edit we train it to multi-task
across an unprecedented range of tasks, such as region-based editing, free-form
editing, and Computer Vision tasks, all of which are formulated as generative
tasks. Additionally, to enhance Emu Edit's multi-task learning abilities, we
provide it with learned task embeddings which guide the generation process
towards the correct edit type. Both these elements are essential for Emu Edit's
outstanding performance. Furthermore, we show that Emu Edit can generalize to
new tasks, such as image inpainting, super-resolution, and compositions of
editing tasks, with just a few labeled examples. This capability offers a
significant advantage in scenarios where high-quality samples are scarce.
Lastly, to facilitate a more rigorous and informed assessment of instructable
image editing models, we release a new challenging and versatile benchmark that
includes seven different image editing tasks.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Learning impartial policies for sequential counterfactual explanations using Deep Reinforcement Learning
Abstract: In the field of explainable Artificial Intelligence (XAI), sequential
counterfactual (SCF) examples are often used to alter the decision of a trained
classifier by implementing a sequence of modifications to the input instance.
Although certain test-time algorithms aim to optimize for each new instance
individually, recently Reinforcement Learning (RL) methods have been proposed
that seek to learn policies for discovering SCFs, thereby enhancing
scalability. As is typical in RL, the formulation of the RL problem, including
the specification of state space, actions, and rewards, can often be ambiguous.
In this work, we identify shortcomings in existing methods that can result in
policies with undesired properties, such as a bias towards specific actions. We
propose to use the output probabilities of the classifier to create a more
informative reward, to mitigate this effect.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Concept-free Causal Disentanglement with Variational Graph Auto-Encoder
Abstract: In disentangled representation learning, the goal is to achieve a compact
representation that consists of all interpretable generative factors in the
observational data. Learning disentangled representations for graphs becomes
increasingly important as graph data rapidly grows. Existing approaches often
rely on Variational Auto-Encoder (VAE) or its causal structure learning-based
refinement, which suffer from sub-optimality in VAEs due to the independence
factor assumption and unavailability of concept labels, respectively. In this
paper, we propose an unsupervised solution, dubbed concept-free causal
disentanglement, built on a theoretically provable tight upper bound
approximating the optimal factor. This results in an SCM-like causal structure
modeling that directly learns concept structures from data. Based on this idea,
we propose Concept-free Causal VGAE (CCVGAE) by incorporating a novel causal
disentanglement layer into Variational Graph Auto-Encoder. Furthermore, we
prove concept consistency under our concept-free causal disentanglement
framework, hence employing it to enhance the meta-learning framework, called
concept-free causal Meta-Graph (CC-Meta-Graph). We conduct extensive
experiments to demonstrate the superiority of the proposed models: CCVGAE and
CC-Meta-Graph, reaching up to $29\%$ and $11\%$ absolute improvements over
baselines in terms of AUC, respectively.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Digital Socrates: Evaluating LLMs through explanation critiques
Abstract: While LLMs can provide reasoned explanations along with their answers, the
nature and quality of those explanations are still poorly understood. In
response, our goal is to define a detailed way of characterizing the
explanation capabilities of modern models and to create a nuanced,
interpretable explanation evaluation tool that can generate such
characterizations automatically, without relying on expensive API calls or
human annotations. Our approach is to (a) define the new task of explanation
critiquing - identifying and categorizing any main flaw in an explanation and
providing suggestions to address the flaw, (b) create a sizeable,
human-verified dataset for this task, and (c) train an open-source, automatic
critiquing model (called Digital Socrates) using this data. Through
quantitative and qualitative analysis, we demonstrate how Digital Socrates is
useful for revealing insights about student models by examining their reasoning
chains, and how it can provide high-quality, nuanced, automatic evaluation of
those model explanations for the first time. Digital Socrates thus fills an
important gap in evaluation tools for understanding and improving the
explanation behavior of models.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Constant-time Motion Planning with Anytime Refinement for Manipulation
Abstract: Robotic manipulators are essential for future autonomous systems, yet limited
trust in their autonomy has confined them to rigid, task-specific systems. The
intricate configuration space of manipulators, coupled with the challenges of
obstacle avoidance and constraint satisfaction, often makes motion planning the
bottleneck for achieving reliable and adaptable autonomy. Recently, a class of
constant-time motion planners (CTMP) was introduced. These planners employ a
preprocessing phase to compute data structures that enable online planning
provably guarantee the ability to generate motion plans, potentially
sub-optimal, within a user defined time bound. This framework has been
demonstrated to be effective in a number of time-critical tasks. However,
robotic systems often have more time allotted for planning than the online
portion of CTMP requires, time that can be used to improve the solution. To
this end, we propose an anytime refinement approach that works in combination
with CTMP algorithms. Our proposed framework, as it operates as a constant time
algorithm, rapidly generates an initial solution within a user-defined time
threshold. Furthermore, functioning as an anytime algorithm, it iteratively
refines the solution's quality within the allocated time budget. This enables
our approach to strike a balance between guaranteed fast plan generation and
the pursuit of optimization over time. We support our approach by elucidating
its analytical properties, showing the convergence of the anytime component
towards optimal solutions. Additionally, we provide empirical validation
through simulation and real-world demonstrations on a 6 degree-of-freedom robot
manipulator, applied to an assembly domain.

Output: Robotics


Instruction: What field is the article from?

Prompt:
Title: Interpretable Prototype-based Graph Information Bottleneck
Abstract: The success of Graph Neural Networks (GNNs) has led to a need for
understanding their decision-making process and providing explanations for
their predictions, which has given rise to explainable AI (XAI) that offers
transparent explanations for black-box models. Recently, the use of prototypes
has successfully improved the explainability of models by learning prototypes
to imply training graphs that affect the prediction. However, these approaches
tend to provide prototypes with excessive information from the entire graph,
leading to the exclusion of key substructures or the inclusion of irrelevant
substructures, which can limit both the interpretability and the performance of
the model in downstream tasks. In this work, we propose a novel framework of
explainable GNNs, called interpretable Prototype-based Graph Information
Bottleneck (PGIB) that incorporates prototype learning within the information
bottleneck framework to provide prototypes with the key subgraph from the input
graph that is important for the model prediction. This is the first work that
incorporates prototype learning into the process of identifying the key
subgraphs that have a critical impact on the prediction performance. Extensive
experiments, including qualitative analysis, demonstrate that PGIB outperforms
state-of-the-art methods in terms of both prediction performance and
explainability.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Dynamic Corrective Self-Distillation for Better Fine-Tuning of Pretrained Models
Abstract: We tackle the challenging issue of aggressive fine-tuning encountered during
the process of transfer learning of pre-trained language models (PLMs) with
limited labeled downstream data. This problem primarily results in a decline in
performance on the subsequent task. Inspired by the adaptive boosting method in
traditional machine learning, we present an effective dynamic corrective
self-distillation (DCS) approach to improve the fine-tuning of the PLMs. Our
technique involves performing a self-distillation mechanism where, at each
iteration, the student model actively adapts and corrects itself by dynamically
adjusting the weights assigned to individual data points. This iterative
self-correcting process significantly enhances the overall fine-tuning
capability of PLMs, leading to improved performance and robustness. We
conducted comprehensive evaluations using the GLUE benchmark demonstrating the
efficacy of our method in enhancing the fine-tuning process for various PLMs
across diverse downstream tasks.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: OC-NMN: Object-centric Compositional Neural Module Network for Generative Visual Analogical Reasoning
Abstract: A key aspect of human intelligence is the ability to imagine -- composing
learned concepts in novel ways -- to make sense of new scenarios. Such capacity
is not yet attained for machine learning systems. In this work, in the context
of visual reasoning, we show how modularity can be leveraged to derive a
compositional data augmentation framework inspired by imagination. Our method,
denoted Object-centric Compositional Neural Module Network (OC-NMN), decomposes
visual generative reasoning tasks into a series of primitives applied to
objects without using a domain-specific language. We show that our modular
architectural choices can be used to generate new training tasks that lead to
better out-of-distribution generalization. We compare our model to existing and
new baselines in proposed visual reasoning benchmark that consists of applying
arithmetic operations to MNIST digits.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Learn to Optimize Denoising Scores for 3D Generation: A Unified and Improved Diffusion Prior on NeRF and 3D Gaussian Splatting
Abstract: We propose a unified framework aimed at enhancing the diffusion priors for 3D
generation tasks. Despite the critical importance of these tasks, existing
methodologies often struggle to generate high-caliber results. We begin by
examining the inherent limitations in previous diffusion priors. We identify a
divergence between the diffusion priors and the training procedures of
diffusion models that substantially impairs the quality of 3D generation. To
address this issue, we propose a novel, unified framework that iteratively
optimizes both the 3D model and the diffusion prior. Leveraging the different
learnable parameters of the diffusion prior, our approach offers multiple
configurations, affording various trade-offs between performance and
implementation complexity. Notably, our experimental results demonstrate that
our method markedly surpasses existing techniques, establishing new
state-of-the-art in the realm of text-to-3D generation. Furthermore, our
approach exhibits impressive performance on both NeRF and the newly introduced
3D Gaussian Splatting backbones. Additionally, our framework yields insightful
contributions to the understanding of recent score distillation methods, such
as the VSD and DDS loss.

Output: Computer Vision