pwc-archive/papers-with-abstracts huggingface dataset:
Airlift Challenge: A Competition for Optimizing Cargo Delivery
Authors: Adis Delanovic, Carmen Chiu, Andre Beckus
Date: 2024-04-26 00:00:00
Tasks: OpenAI Gym

Airlift operations require the timely distribution of various cargo, much of which is time sensitive and valuable. However, these operations have to contend with sudden disruptions from weather and malfunctions, requiring immediate rescheduling. The Airlift Challenge competition seeks possible solutions via a simulator that provides a simplified abstraction of the airlift problem. The simulator uses an OpenAI gym interface that allows participants to create an algorithm for planning agent actions. The algorithm is scored using a remote evaluator against scenarios of ever-increasing difficulty. The second iteration of the competition was underway from November 2023 to April 2024. In this paper, we describe the competition and simulation environment. As a step towards applying generalized planning techniques to the problem, we present a temporal PDDL domain for the Pickup and Delivery Problem, a model which lies at the core of the Airlift Challenge.
Seizing the Means of Production: Exploring the Landscape of Crafting, Adapting and Navigating Generative AI Models in the Visual Arts
Authors: Ahmed M. Abuzuraiq, Philippe Pasquier
Date: 2024-04-26 00:00:00

In this paper, we map out the landscape of options available to visual artists for creating personal artworks, including crafting, adapting and navigating deep generative models. Following that, we argue for revisiting model crafting, defined as the design and manipulation of generative models for creative goals, and motivate studying and designing for model crafting as a creative activity in its own right.
Knowledge Transfer for Cross-Domain Reinforcement Learning: A Systematic Review
Authors: Sergio A. Serrano, Jose Martinez-Carranza, L. Enrique Sucar
Date: 2024-04-26 00:00:00
Tasks: Decision Making, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Transfer Learning

Reinforcement Learning (RL) provides a framework in which agents can be trained, via trial and error, to solve complex decision-making problems. Learning with little supervision causes RL methods to require large amounts of data, rendering them too expensive for many applications (e.g., robotics). By reusing knowledge from a different task, knowledge transfer methods present an alternative to reduce the training time in RL. Given the severe data scarcity, due to their flexibility, there has been a growing interest in methods capable of transferring knowledge across different domains (i.e., problems with different representations). However, identifying similarities and adapting knowledge across tasks from different domains requires matching their representations or finding domain-invariant features. These processes can be data-demanding, which poses the main challenge in cross-domain knowledge transfer: to select and transform knowledge in a data-efficient way, such that it accelerates learning in the target task, despite the presence of significant differences across problems (e.g., robots with distinct morphologies). Thus, this review presents a unifying analysis of methods focused on transferring knowledge across different domains. Through a taxonomy based on a transfer-approach categorization and a characterization of works based on their data-assumption requirements, the contributions of this article are 1) a comprehensive and systematic revision of knowledge transfer methods for the cross-domain RL setting, 2) a categorization and characterization of such methods to provide an analysis based on relevant features such as their transfer approach and data requirements, and 3) a discussion on the main challenges regarding cross-domain knowledge transfer, as well as on ideas of future directions worth exploring to address these problems.
Center-Based Relaxed Learning Against Membership Inference Attacks
Authors: Xingli Fang, Jung-eun Kim
Date: 2024-04-26 00:00:00

Membership inference attacks (MIAs) are currently considered one of the main privacy attack strategies, and their defense mechanisms have also been extensively explored. However, there is still a gap between the existing defense approaches and ideal models in performance and deployment costs. In particular, we observed that the privacy vulnerability of the model is closely correlated with the gap between the model's data-memorizing ability and generalization ability. To address this, we propose a new architecture-agnostic training paradigm called center-based relaxed learning (CRL), which is adaptive to any classification model and provides privacy preservation by sacrificing a minimal or no loss of model generalizability. We emphasize that CRL can better maintain the model's consistency between member and non-member data. Through extensive experiments on standard classification datasets, we empirically show that this approach exhibits comparable performance without requiring additional model capacity or data costs.
Federated Learning for Blind Image Super-Resolution
Authors: Brian B. Moser, Ahmed Anwar, Federico Raue, Stanislav Frolov, Andreas Dengel
Date: 2024-04-26 00:00:00
Tasks: Federated Learning, Image Super-Resolution, Super-Resolution

Traditional blind image SR methods need to model real-world degradations precisely. Consequently, current research struggles with this dilemma by assuming idealized degradations, which leads to limited applicability to actual user data. Moreover, the ideal scenario - training models on data from the targeted user base - presents significant privacy concerns. To address both challenges, we propose to fuse image SR with federated learning, allowing real-world degradations to be directly learned from users without invading their privacy. Furthermore, it enables optimization across many devices without data centralization. As this fusion is underexplored, we introduce new benchmarks specifically designed to evaluate new SR methods in this federated setting. By doing so, we employ known degradation modeling techniques from SR research. However, rather than aiming to mirror real degradations, our benchmarks use these degradation models to simulate the variety of degradations found across clients within a distributed user base. This distinction is crucial as it circumvents the need to precisely model real-world degradations, which limits contemporary blind image SR research. Our proposed benchmarks investigate blind image SR under new aspects, namely differently distributed degradation types among users and varying user numbers. We believe new methods tested within these benchmarks will perform more similarly in an application, as the simulated scenario addresses the variety while federated learning enables the training on actual degradations.
Consolidating LAMA with Best-First Width Search
Authors: Augusto B. Corrêa, Jendrik Seipp
Date: 2024-04-26 00:00:00
Tasks: Heuristic Search

One key decision for heuristic search algorithms is how to balance exploration and exploitation. In classical planning, novelty search has come out as the most successful approach in this respect. The idea is to favor states that contain previously unseen facts when searching for a plan. This is done by maintaining a record of the tuples of facts observed in previous states. Then the novelty of a state is the size of the smallest previously unseen tuple. The most successful version of novelty search is best-first width search (BFWS), which combines novelty measures with heuristic estimates. An orthogonal approach to balance exploration-exploitation is to use several open-lists. These open-lists are ordered using different heuristic estimates, which diversify the information used in the search. The search algorithm then alternates between these open-lists, trying to exploit these different estimates. This is the approach used by LAMA, a classical planner that, a decade after its release, is still considered state-of-the-art in agile planning. In this paper, we study how to combine LAMA and BFWS. We show that simply adding the strongest open-list used in BFWS to LAMA harms performance. However, we show that combining only parts of each planner leads to a new state-of-the-art agile planner.
A Conditional Independence Test in the Presence of Discretization
Authors: Boyang Sun, Yu Yao, Guang-Yuan Hao, Yumou Qiu, Kun Zhang
Date: 2024-04-26 00:00:00
Tasks: Causal Discovery

Testing conditional independence has many applications, such as in Bayesian network learning and causal discovery. Different test methods have been proposed. However, existing methods generally can not work when only discretized observations are available. Specifically, consider $X_1$, $\tilde{X}_2$ and $X_3$ are observed variables, where $\tilde{X}_2$ is a discretization of latent variables $X_2$. Applying existing test methods to the observations of $X_1$, $\tilde{X}_2$ and $X_3$ can lead to a false conclusion about the underlying conditional independence of variables $X_1$, $X_2$ and $X_3$. Motivated by this, we propose a conditional independence test specifically designed to accommodate the presence of such discretization. To achieve this, we design the bridge equations to recover the parameter reflecting the statistical information of the underlying latent continuous variables. An appropriate test statistic and its asymptotic distribution under the null hypothesis of conditional independence have also been derived. Both theoretical results and empirical validation have been provided, demonstrating the effectiveness of our test methods.
Empowering Large Language Models for Textual Data Augmentation
Authors: Yichuan Li, Kaize Ding, Jianling Wang, Kyumin Lee
Date: 2024-04-26 00:00:00
Tasks: Data Augmentation, Diversity, Few-Shot Learning

With the capabilities of understanding and executing natural language instructions, Large language models (LLMs) can potentially act as a powerful tool for textual data augmentation. However, the quality of augmented data depends heavily on the augmentation instructions provided, and the effectiveness can fluctuate across different downstream tasks. While manually crafting and selecting instructions can offer some improvement, this approach faces scalability and consistency issues in practice due to the diversity of downstream tasks. In this work, we address these limitations by proposing a new solution, which can automatically generate a large pool of augmentation instructions and select the most suitable task-informed instructions, thereby empowering LLMs to create high-quality augmented data for different downstream tasks. Empirically, the proposed approach consistently generates augmented data with better quality compared to non-LLM and LLM-based data augmentation methods, leading to the best performance on 26 few-shot learning tasks sourced from a wide range of application domains.
Alice's Adventures in a Differentiable Wonderland -- Volume I, A Tour of the Land
Authors: Simone Scardapane
Date: 2024-04-26 00:00:00

Neural networks surround us, in the form of large language models, speech transcription systems, molecular discovery algorithms, robotics, and much more. Stripped of anything else, neural networks are compositions of differentiable primitives, and studying them means learning how to program and how to interact with these models, a particular example of what is called differentiable programming. This primer is an introduction to this fascinating field imagined for someone, like Alice, who has just ventured into this strange differentiable wonderland. I overview the basics of optimizing a function via automatic differentiation, and a selection of the most common designs for handling sequences, graphs, texts, and audios. The focus is on a intuitive, self-contained introduction to the most important design techniques, including convolutional, attentional, and recurrent blocks, hoping to bridge the gap between theory and code (PyTorch and JAX) and leaving the reader capable of understanding some of the most advanced models out there, such as large language models (LLMs) and multimodal architectures.
FashionSD-X: Multimodal Fashion Garment Synthesis using Latent Diffusion
Authors: Abhishek Kumar Singh, Ioannis Patras
Date: 2024-04-26 00:00:00
Tasks: Virtual Try-on

The rapid evolution of the fashion industry increasingly intersects with technological advancements, particularly through the integration of generative AI. This study introduces a novel generative pipeline designed to transform the fashion design process by employing latent diffusion models. Utilizing ControlNet and LoRA fine-tuning, our approach generates high-quality images from multimodal inputs such as text and sketches. We leverage and enhance state-of-the-art virtual try-on datasets, including Multimodal Dress Code and VITON-HD, by integrating sketch data. Our evaluation, utilizing metrics like FID, CLIP Score, and KID, demonstrates that our model significantly outperforms traditional stable diffusion models. The results not only highlight the effectiveness of our model in generating fashion-appropriate outputs but also underscore the potential of diffusion models in revolutionizing fashion design workflows. This research paves the way for more interactive, personalized, and technologically enriched methodologies in fashion design and representation, bridging the gap between creative vision and practical application.
Beyond Traditional Threats: A Persistent Backdoor Attack on Federated Learning
Authors: Tao Liu, Yuhang Zhang, Zhu Feng, Zhiqin Yang, Chen Xu, Dapeng Man, Wu Yang
Date: 2024-04-26 00:00:00
Tasks: Backdoor Attack, Federated Learning

Backdoors on federated learning will be diluted by subsequent benign updates. This is reflected in the significant reduction of attack success rate as iterations increase, ultimately failing. We use a new metric to quantify the degree of this weakened backdoor effect, called attack persistence. Given that research to improve this performance has not been widely noted,we propose a Full Combination Backdoor Attack (FCBA) method. It aggregates more combined trigger information for a more complete backdoor pattern in the global model. Trained backdoored global model is more resilient to benign updates, leading to a higher attack success rate on the test set. We test on three datasets and evaluate with two models across various settings. FCBA's persistence outperforms SOTA federated learning backdoor attacks. On GTSRB, postattack 120 rounds, our attack success rate rose over 50% from baseline. The core code of our method is available at https://github.com/PhD-TaoLiu/FCBA.
MetaSD: A Unified Framework for Scalable Downscaling of Meteorological Variables in Diverse Situations
Authors: Jing Hu, Honghu Zhang, Peng Zheng, Jialin Mu, Xiaomeng Huang, Xi Wu
Date: 2024-04-26 00:00:00
Tasks: Meta-Learning

Addressing complex meteorological processes at a fine spatial resolution requires substantial computational resources. To accelerate meteorological simulations, researchers have utilized neural networks to downscale meteorological variables from low-resolution simulations. Despite notable advancements, contemporary cutting-edge downscaling algorithms tailored to specific variables. Addressing meteorological variables in isolation overlooks their interconnectedness, leading to an incomplete understanding of atmospheric dynamics. Additionally, the laborious processes of data collection, annotation, and computational resources required for individual variable downscaling are significant hurdles. Given the limited versatility of existing models across different meteorological variables and their failure to account for inter-variable relationships, this paper proposes a unified downscaling approach leveraging meta-learning. This framework aims to facilitate the downscaling of diverse meteorological variables derived from various numerical models and spatiotemporal scales. Trained at variables consisted of temperature, wind, surface pressure and total precipitation from ERA5 and GFS, the proposed method can be extended to downscale convective precipitation, potential energy, height, humidity and ozone from CFS, S2S and CMIP6 at different spatiotemporal scales, which demonstrating its capability to capture the interconnections among diverse variables. Our approach represents the initial effort to create a generalized downscaling model. Experimental evidence demonstrates that the proposed model outperforms existing top downscaling methods in both quantitative and qualitative assessments.
CoSD: Collaborative Stance Detection with Contrastive Heterogeneous Topic Graph Learning
Authors: Yinghan Cheng, Qi Zhang, Chongyang Shi, Liang Xiao, Shufeng Hao, Liang Hu
Date: 2024-04-26 00:00:00
Tasks: Graph Learning, Stance Detection

Stance detection seeks to identify the viewpoints of individuals either in favor or against a given target or a controversial topic. Current advanced neural models for stance detection typically employ fully parametric softmax classifiers. However, these methods suffer from several limitations, including lack of explainability, insensitivity to the latent data structure, and unimodality, which greatly restrict their performance and applications. To address these challenges, we present a novel collaborative stance detection framework called (CoSD) which leverages contrastive heterogeneous topic graph learning to learn topic-aware semantics and collaborative signals among texts, topics, and stance labels for enhancing stance detection. During training, we construct a heterogeneous graph to structurally organize texts and stances through implicit topics via employing latent Dirichlet allocation. We then perform contrastive graph learning to learn heterogeneous node representations, aggregating informative multi-hop collaborative signals via an elaborate Collaboration Propagation Aggregation (CPA) module. During inference, we introduce a hybrid similarity scoring module to enable the comprehensive incorporation of topic-aware semantics and collaborative signals for stance detection. Extensive experiments on two benchmark datasets demonstrate the state-of-the-art detection performance of CoSD, verifying the effectiveness and explainability of our collaborative framework.
Utilizing Large Language Models to Identify Reddit Users Considering Vaping Cessation for Digital Interventions
Authors: Sai Krishna Revanth Vuruma, Dezhi Wu, Saborny Sen Gupta, Lucas Aust, Valerie Lookingbill, Caleb Henry, Yang Ren, Erin Kasson, Li-Shiun Chen, Patricia Cavazos-Rehg, Dian Hu, Ming Huang
Date: 2024-04-25 00:00:00
Tasks: Human Detection, Sentence

The widespread adoption of social media platforms globally not only enhances users' connectivity and communication but also emerges as a vital channel for the dissemination of health-related information, thereby establishing social media data as an invaluable organic data resource for public health research. The surge in popularity of vaping or e-cigarette use in the United States and other countries has caused an outbreak of e-cigarette and vaping use-associated lung injury (EVALI), leading to hospitalizations and fatalities in 2019, highlighting the urgency to comprehend vaping behaviors and develop effective strategies for cession. In this study, we extracted a sample dataset from one vaping sub-community on Reddit to analyze users' quit vaping intentions. Leveraging large language models including both the latest GPT-4 and traditional BERT-based language models for sentence-level quit-vaping intention prediction tasks, this study compares the outcomes of these models against human annotations. Notably, when compared to human evaluators, GPT-4 model demonstrates superior consistency in adhering to annotation guidelines and processes, showcasing advanced capabilities to detect nuanced user quit-vaping intentions that human evaluators might overlook. These preliminary findings emphasize the potential of GPT-4 in enhancing the accuracy and reliability of social media data analysis, especially in identifying subtle users' intentions that may elude human detection.
Autonomous LLM-driven research from data to human-verifiable research papers
Authors: Tal Ifargan, Lukas Hafner, Maor Kern, Ori Alcalay, Roy Kishony
Date: 2024-04-24 00:00:00
Tasks: scientific discovery

As AI promises to accelerate scientific discovery, it remains unclear whether fully AI-driven research is possible and whether it can adhere to key scientific values, such as transparency, traceability and verifiability. Mimicking human scientific practices, we built data-to-paper, an automation platform that guides interacting LLM agents through a complete stepwise research process, while programmatically back-tracing information flow and allowing human oversight and interactions. In autopilot mode, provided with annotated data alone, data-to-paper raised hypotheses, designed research plans, wrote and debugged analysis codes, generated and interpreted results, and created complete and information-traceable research papers. Even though research novelty was relatively limited, the process demonstrated autonomous generation of de novo quantitative insights from data. For simple research goals, a fully-autonomous cycle can create manuscripts which recapitulate peer-reviewed publications without major errors in about 80-90%, yet as goal complexity increases, human co-piloting becomes critical for assuring accuracy. Beyond the process itself, created manuscripts too are inherently verifiable, as information-tracing allows to programmatically chain results, methods and data. Our work thereby demonstrates a potential for AI-driven acceleration of scientific discovery while enhancing, rather than jeopardizing, traceability, transparency and verifiability.
Hallucination of Multimodal Large Language Models: A Survey
Authors: Zechen Bai, Pichao Wang, Tianjun Xiao, Tong He, Zongbo Han, Zheng Zhang, Mike Zheng Shou
Date: 2024-04-29 00:00:00
Tasks: Hallucination, Survey

This survey presents a comprehensive analysis of the phenomenon of hallucination in multimodal large language models (MLLMs), also known as Large Vision-Language Models (LVLMs), which have demonstrated significant advancements and remarkable abilities in multimodal tasks. Despite these promising developments, MLLMs often generate outputs that are inconsistent with the visual content, a challenge known as hallucination, which poses substantial obstacles to their practical deployment and raises concerns regarding their reliability in real-world applications. This problem has attracted increasing attention, prompting efforts to detect and mitigate such inaccuracies. We review recent advances in identifying, evaluating, and mitigating these hallucinations, offering a detailed overview of the underlying causes, evaluation benchmarks, metrics, and strategies developed to address this issue. Additionally, we analyze the current challenges and limitations, formulating open questions that delineate potential pathways for future research. By drawing the granular classification and landscapes of hallucination causes, evaluation benchmarks, and mitigation methods, this survey aims to deepen the understanding of hallucinations in MLLMs and inspire further advancements in the field. Through our thorough and in-depth review, we contribute to the ongoing dialogue on enhancing the robustness and reliability of MLLMs, providing valuable insights and resources for researchers and practitioners alike. Resources are available at: https://github.com/showlab/Awesome-MLLM-Hallucination.