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Risk Assessment and Statistical Significance in the Age of Foundation Models We propose a distributional framework for assessing socio-technical risks of foundation models with quantified statistical significance. Our approach hinges on a new statistical relative testing based on first and second order stochastic domi... | 0 |
Complex priors and flexible inference in recurrent circuits with dendritic nonlinearities Despite many successful examples in which probabilistic inference can account for perception, we have little understanding of how the brain represents and uses structured priors that capture the complexity of natural input statist... | 1 |
TransCues: Boundary and Reflection-empowered Pyramid Vision Transformer for Semantic Transparent Object Segmentation Although glass is a prevalent material in everyday life, most semantic segmentation methods struggle to distinguish it from opaque materials. We propose $\textbf{TransCues}$, a pyramidal transformer enco... | 0 |
Imitation Learning Using Generalized Sliced Wasserstein Distances Imitation learning methods allow to train reinforcement learning policies by way
of minimizing a divergence measure between the state occupancies of the expert
agent and the novice policy. Alternatively, a true metric in the space of probability
measures... | 0 |
VFLAIR: A Research Library and Benchmark for Vertical Federated Learning Vertical Federated Learning (VFL) has emerged as a collaborative training paradigm that allows participants with different features of the same group of users to accomplish cooperative training without exposing their raw data or model parameters. ... | 1 |
Poisoning-based Backdoor Attack against Vision-Language Model We delve into a novel methodology of performing stealthy backdoor attacks on large language models under visual instruction tuning, wherein we subtly infuse malicious triggers in both the textual and visual domains, thereby manipulating the model to exhibit ... | 0 |
Emergent representations in networks trained with the Forward-Forward algorithm The Backpropagation algorithm, widely used to train neural networks, has often been criticised for its lack of biological realism. In an attempt to find a more biologically plausible alternative, and avoid to back-propagate gradients in fav... | 0 |
CASR: Refining Action Segmentation via marginalizing frame-level causal relationships Integrating deep learning and causal discovery has increased the necessity for a causal relationship between frames as evidence for explainability in Temporal Action Segmentation (TAS) tasks. However, frame-level causal relationships ... | 0 |
AdaMerging: Adaptive Model Merging for Multi-Task Learning Multi-task learning (MTL) aims to empower a model to tackle multiple tasks simultaneously. A recent development known as task arithmetic has revealed that several models, each fine-tuned for distinct tasks, can be directly merged into a single model to execute ... | 1 |
Object-Relational Graph Framework for Zero-Shot 3D Scene Segmentation When it comes to understanding 3D scenes, the capability to perform zero-shot comprehension on real-world objects is essential, as unseen objects, absent from the training data, frequently appear in natural scenes. While prior works have proposed ze... | 0 |
Vision Transformers Need Registers Transformers have recently emerged as a powerful tool for learning visual representations. In this paper, we identify and characterize artifacts in feature maps of both supervised and self-supervised ViT networks. The artifacts correspond to high-norm tokens appearing during inference... | 1 |
The Program Testing Ability of Large Language Models for Code Recent development of large language models (LLMs) for code like CodeX and CodeT5+ demonstrates tremendous promise in achieving code intelligence. Their ability of synthesizing code that completes a program for performing a pre-defined task has been intensiv... | 0 |
LayerNAS: Neural Architecture Search in Polynomial Complexity Neural Architecture Search (NAS) has become a popular method for discovering effective model architectures, especially for target hardware. As such, NAS methods that find optimal architectures under constraints are essential. In our paper, we propose LayerNA... | 0 |
SAM-guided Unsupervised Domain Adaptation for 3D Segmentation Unsupervised domain adaptation (UDA) in 3D segmentation tasks presents a formidable challenge, primarily stemming from the sparse and unordered nature of point cloud data. Especially for LiDAR point clouds, the domain discrepancy becomes obvious across vary... | 0 |
Understanding Domain Generalization: A Noise Robustness Perspective Despite the rapid development of machine learning algorithms for domain generalization (DG), there is no clear empirical evidence that the existing DG algorithms outperform the classic empirical risk minimization (ERM) across standard benchmarks. To be... | 1 |
Multi-Resolution Active Learning of Fourier Neural Operators Fourier Neural Operator (FNO) is a popular operator learning framework, which not only achieves the state-of-the-art performance in many tasks, but also is highly efficient in training and prediction. However, collecting training data for the FNO is a costly ... | 0 |
ADOPT: Modified Adam Can Converge with the Optimal Rate with Any Hyperparameters Adaptive gradient methods based on exponential moving averages, such as Adam and RMSprop, are widely used for deep learning. However, it is known that they do not converge unless choosing hyperparameters in a problem-dependent manner. Ther... | 0 |
Flatness-aware Adversarial Attack The transferability of adversarial examples can be exploited to launch black-box attacks. However, adversarial ones often present poor transferability. To alleviate this issue, by observing that the diversity of inputs can boost transferability, input regularization based methods are p... | 0 |
G-Local Attention Graph Pooling for Graph Classification Graph pooling is an essential operation in Graph Neural Networks that reduces the size of an input graph while preserving its core structural properties. This compression operation improves the learned representation of the graph, yielding to a performance boost ... | 0 |
Enhancing Human-AI Collaboration Through Logic-Guided Reasoning We present a systematic framework designed to enhance human-robot perception and collaboration through the integration of logical rules and Theory of Mind (ToM). Logical rules provide interpretable predictions and generalize well across diverse tasks, maki... | 1 |
Investigating the Ability of PINNs To Solve Burgers' PDE Near Finite-Time BlowUp Physics Informed Neural Networks (PINNs) have been achieving ever newer feats of solving complicated PDEs numerically while offering an attractive trade-off between accuracy and speed of inference. A particularly challenging aspect of PDEs... | 0 |
Segment, Select, Correct: A Framework for Weakly-Supervised Referring Segmentation Referring Image Segmentation (RIS) - the problem of identifying objects in images through natural language sentences - is a challenging task currently mostly solved through supervised learning. However, while collecting referred annotati... | 0 |
Fooling the Textual Fooler via Randomizing Latent Representations Despite outstanding performance in a variety of NLP tasks, recent studies have revealed that NLP models are vulnerable to adversarial attacks that slightly perturb the input to cause the models to misbehave. Among these attacks, adversarial word-level pe... | 0 |
Frequency-Aware Masked Autoencoders for Multimodal Pretraining on Biosignals Leveraging multimodal information from biosignals is vital for building a comprehensive representation of people's physical and mental states. However, multimodal biosignals often exhibit substantial distributional shifts between pretraining a... | 0 |
Splicing Up Your Predictions with RNA Contrastive Learning In the face of rapidly accumulating genomic data, our understanding of the RNA regulatory code remains incomplete. Recent self-supervised methods in other domains have demonstrated the ability to learn rules underlying the data-generating process such as senten... | 0 |
ENHANCEMENT OF GNN’S EXPRESSIVE POWER VIA RECONSIDERING MODAL LOGIC Since AC-GNNs, in which nodes only gather information from their neighbors to update features at each layer, are limited in their expressive power, numerous models have been proposed to enable GNNs to go beyond Weisfeiler-Lehman (WL) test. However ther... | 0 |
Solving Diffusion ODEs with Optimal Boundary Conditions for Better Image Super-Resolution Diffusion models, as a kind of powerful generative model, have given impressive results on image super-resolution (SR) tasks. However, due to the randomness introduced in the reverse process of diffusion models, the performances o... | 1 |
LipVoicer: Generating Speech from Silent Videos Guided by Lip Reading Lip-to-speech involves generating a natural-sounding speech synchronized with a soundless video of a person talking. Despite recent advances, current methods still cannot produce high-quality speech with high levels of intelligibility for challenging... | 1 |
HyperMask: Adaptive Hypernetwork-based Masks for Continual Learning Artificial neural networks suffer catastrophic forgetting when they are sequentially trained on multiple tasks. To overcome this problem, there exist many continual learning strategies. One of the most effective is the hypernetwork-based approach. The... | 0 |
Wigner kernels: body-ordered equivariant machine learning without a basis Machine-learning models based on a point-cloud representation of a physical object are ubiquitous in scientific applications and particularly well-suited to the atomic-scale description of molecules and materials. Among the many different approac... | 0 |
Neural Networks and Solomonoff Induction Solomonoff Induction (SI) is the most powerful universal predictor given unlimited computational resources. Naive SI approximations are challenging and require running vast amount of programs for extremely long. Here we explore an alternative path to SI
consisting in meta-tra... | 0 |
In-context Curriculum for Mathematical Reasoning in Small Language Models Specializing Small Language Models (SLMs) in mathematical reasoning improves the scaling of model performance and reduces the cost of inference. Leveraging the model's context is key for specialization and parameter-free adaptation in the In-cont... | 0 |
A New Theoretical Perspective on Data Heterogeneity in Federated Averaging In federated learning, data heterogeneity is the main reason that existing theoretical analyses are pessimistic about the convergence error caused by local updates. However, empirical studies have shown that more local updates can improve the co... | 0 |
Enabling Efficient Equivariant Operations in the Fourier Basis via Gaunt Tensor Products Developing equivariant neural networks for the E(3) group plays an important role in modeling 3D data across real-world applications. Enforcing this equivariance primarily involves the tensor products of irreducible representations... | 1 |
ILPO-NET: convolution network for the recognition of arbitrary volumetric patterns Modern spatial data analysis is built on the effective recognition of spatial patterns and learning their hierarchy. Applications to real-world volumetric data require techniques that ensure invariance not only to shifts but also to patt... | 0 |
Decentralized Riemannian Conjugate Gradient Method on the Stiefel Manifold The conjugate gradient method is a crucial first-order optimization method that generally converges faster than the steepest descent method, and its computational cost is much lower than that of second-order methods. However, while various types... | 1 |
LLCP: Learning Latent Causal Processes for Reasoning-based Video Question Answer Current approaches to Video Question Answering (VideoQA) primarily focus on cross-modality matching, which is limited by the requirement for extensive data annotations and the insufficient capacity for causal reasoning (e.g. attributing ac... | 1 |
InstaTAP: Instance Motion Estimation for Tracking Any Point This paper tackles a challenge in learning the long-term point trajectories in videos, like the Tracking Any Point (TAP) task. Fundamentally, the estimation of point-level motions is hindered by the significant uncertainty inherent in comprehensive comparisons... | 0 |
Automatic Calibration and Error Correction for Generative Large Language Models via Pareto Optimal Self-Supervision Generative Large language models (LLMs) have demonstrated remarkable capabilities for a wide range of applications, but reducing ungrounded or erroneous responses remains a major growth area. Unlike task-... | 0 |
Rethinking Backdoor Attacks on Dataset Distillation: A Kernel Method Perspective Dataset distillation offers a potential means to enhance data efficiency in deep learning. Recent studies have shown its ability to counteract backdoor risks present in original training samples. In this study, we delve into the theoretica... | 1 |
WaveFluid: A New Adversarial Approach for Efficient High-Fidelity Speech Synthesis Probability flow based models for image and audio synthesis, such as denoising diffusion probabilistic models and poisson flow generative models, can be interpreted as modeling the ground truth distribution through a non-compressive pass... | 0 |
Skip-Attention: Improving Vision Transformers by Paying Less Attention This work aims to improve the efficiency of vision transformers (ViTs). While ViTs use computationally expensive self-attention operations in every layer, we identify that these operations are highly correlated across layers -- a key redundancy th... | 1 |
EvIL: Evolution Strategies for Generalisable Imitation Learning We present Evolutionary Imitation Learning (EvIL), a general approach to imitation learning (IL) able to predict agent behaviour across changing environment dynamics. In EvIL, we use Evolution Strategies to jointly meta-optimise the parameters (e.g. reward... | 0 |
Learning Hierarchical Image Segmentation For Recognition and By Recognition Large vision and language models learned directly through image-text associations often lack detailed visual substantiation, whereas image segmentation tasks are treated separately from recognition, supervisedly learned without interconnections... | 1 |
A Large-Scale 3D Face Mesh Video Dataset via Neural Re-parameterized Optimization We propose NeuFace, a 3D face mesh pseudo annotation method on videos via neural re-parameterized optimization. Despite the huge progress in 3D face reconstruction methods, generating reliable 3D face labels for in-the-wild dynamic videos... | 0 |
PETformer: Long-term Time Series Forecasting via Placeholder-enhanced Transformer Recently, the superiority of Transformer for long-term time series forecasting (LTSF) tasks has been challenged, particularly since recent work has shown that simple models can outperform numerous Transformer-based approaches. This sugges... | 0 |
One by One, Continual Coordinating with Humans via Hyper-Teammate Identification One of the primary objectives in modern artificial intelligence researches is to empower agents to effectively coordinate with diverse teammates, particularly human teammates. Previous studies focused on training agents either with a fixe... | 0 |
Pretrained deep models outperform GBDTs in Learning-To-Rank under label scarcity While deep learning (DL) models are state-of-the-art in text and image domains, they have not yet consistently outperformed Gradient Boosted Decision Trees (GBDTs) on tabular Learning-To-Rank (LTR) problems. Most of the recent performance ... | 0 |
Investigating the Fairness of Large Language Models for Predictions on Tabular Data Recent literature has suggested the potential of using large language models (LLMs) to make predictions for tabular tasks. However, LLMs have been shown to exhibit harmful social biases that reflect the stereotypes and inequalities pres... | 0 |
MG-TSD: Multi-Granularity Time Series Diffusion Models with Guided Learning Process Recently, diffusion probabilistic models have attracted attention in generative time series forecasting due to their remarkable capacity to generate high-fidelity samples. However, the effective utilization of their strong modeling abil... | 1 |
GeoMFormer: A General Architecture for Geometric Molecular Representation Learning Molecular modeling, a central topic in quantum mechanics, aims to accurately calculate the properties and simulate the behaviors of molecular systems. The molecular model is governed by physical laws, which impose geometric constraints s... | 0 |
Transformers Perform In-Context Learning through Neural Networks Transformer based neural sequence models exhibit remarkable ability to do in-context learning. Given some training examples, a pre-trained model can make accurate predictions on a novel input. This paper studies why transformers can learn different types ... | 0 |
Estimating Performative Effects in Dynamical Systems: the advantage of sequential observations Regulators and academics are increasingly interested in the causal effect that algorithmic actions of a digital platform have on consumption, a quantity in the machine learning literature termed the performative effect. In th... | 0 |
Learning interpretable control inputs and dynamics underlying animal locomotion A central objective in neuroscience is to understand how the brain orchestrates movement. Recent advances in automated tracking technologies have made it possible to document behavior with unprecedented temporal resolution and scale, genera... | 1 |
Boosting Unsupervised Contrastive Learning Using Diffusion-Based Data Augmentation From Scratch Unsupervised Contrastive learning has gained prominence in fields such as vision, natural language processing, and biology, leveraging predefined positive and negative samples for representation learning. Data augmentation, ... | 0 |
Leftover Lunch: Advantage-based Offline Reinforcement Learning for Language Models Reinforcement Learning with Human Feedback (RLHF) is the most prominent method for Language Model (LM) alignment. However, RLHF is an unstable and data-hungry process that continually requires new high-quality LM-generated data for finet... | 1 |
On the hardness of learning under symmetries We study the problem of learning equivariant neural networks via gradient descent. The incorporation of known symmetries ("equivariance") into neural nets has empirically improved the performance of learning pipelines, in domains ranging from biology to computer vision. How... | 1 |
Semantic Attribution For Explainable Uncertainty Quantification Bayesian deep learning, with an emphasis on uncertainty quantification, is receiving growing interest in building reliable models. Nonetheless, interpreting and explaining the origins and reasons for uncertainty presents a significant challenge. In this pa... | 0 |
Probabilistic Sampling-Enhanced Temporal-Spatial GCN: A Scalable Framework for Transaction Anomaly Detection in Ethereum Networks The rapid evolution of the Ethereum network necessitates sophisticated techniques to ensure its robustness against potential threats and to maintain transparency. While Graph Neural Networks... | 0 |
Posterior Probability-Based Label Recovery Attack in Federated Learning Recent works have proposed analytical attacks that can recover batch labels from gradients of a classification model in Federated Learning. However, these studies do not explain the essence of label leakage or show the scalability of other classifi... | 0 |
Adaptive Compression of the Latent Space in Variational Autoencoders Variational Autoencoders (VAEs) are powerful generative models that have been widely used in various fields, including image and text generation. However, one of the known challenges in using VAEs is the model's sensitivity to its hyperparameters, suc... | 0 |
Improving classifier decision boundaries using nearest neighbors In this paper, we show that neural networks are not learning optimal decision boundaries. Decision boundaries go through areas of low training data density. They are impacted by few training samples which can easily lead to overfitting. We show that perfo... | 0 |
A Data-Driven Measure of Relative Uncertainty for Misclassification Detection Misclassification detection is an important problem in machine learning, as it allows for the identification of instances where the model's predictions are unreliable. However, conventional uncertainty measures such as Shannon entropy do not ... | 1 |
Learning Sequence Attractors in Recurrent Networks with Hidden Neurons The brain is targeted for processing temporal sequence information. It remains largely unclear how the brain learns to store and retrieve sequence memories. Here, we study how recurrent networks of binary neurons learn sequence attractors to store p... | 0 |
CITING: Large Language Models Create Curriculum for Instruction Tuning The recent advancement of large language models (LLMs) has been achieved through a combo of instruction tuning and human alignment. However, building manually crafted instruction datasets and performing human alignment become the bottleneck for scal... | 0 |
Explaining black box text modules in natural language with language models Large language models (LLMs) have demonstrated remarkable prediction performance for a growing array of tasks. However, their rapid proliferation and increasing opaqueness have created a growing need for interpretability. Here, we ask whether we... | 0 |
UNLEARNING THE UNWANTED DATA FROM A PERSONALIZED RECOMMENDATION MODEL Recommender Systems (RS) learn user behavior by monitoring their activities on the online platform. In a few scenarios, users consume the content but don’t want to get their recommendations because a). They consumed the content by mistake, and those ... | 0 |
UniPAD: A Universal Pre-training Paradigm for Autonomous Driving In the context of autonomous driving, the significance of effective feature learning is widely acknowledged. While conventional 3D self-supervised pre-training methods have shown widespread success, most methods follow the ideas originally designed for 2D... | 0 |
InfoBatch: Lossless Training Speed Up by Unbiased Dynamic Data Pruning Data pruning aims to obtain lossless performances with less overall cost. A common approach is to filter out samples that make less contribution to the training. This could lead to gradient expectation bias compared to the original data. To solve th... | 1 |
Symbolic equation solving via reinforcement learning Machine-learning methods are gradually being adopted in a great variety of social, economic, and scientific contexts, yet they are notorious for struggling with exact mathematics. A typical example is computer algebra, which includes tasks like simplifying mathematic... | 0 |
Free-style and Fast 3D Portrait Synthesis Efficiently generating a free-style 3D portrait with high quality and consistency is a promising yet challenging task. The portrait styles generated by most existing methods are usually restricted by their 3D generators, which are learned in specific facial datasets, such as FF... | 0 |
Hierarchical GFlownet for Crystal Structure Generation Discovering new solid-state materials necessitates the ability to rapidly explore the vast space of crystal structures and locate stable regions. Generating stable materials with desired properties and composition is a challenging task because of (a) the exponentia... | 0 |
CoT3DRef: Chain-of-Thoughts Data-Efficient 3D Visual Grounding 3D visual grounding is the ability to localize objects in 3D scenes conditioned by utterances. Most existing methods devote the referring head to localize the referred object directly, causing failure in complex scenarios. In addition, it does not illustrat... | 1 |
Dual RL: Unification and New Methods for Reinforcement and Imitation Learning The goal of reinforcement learning (RL) is to find a policy that maximizes the expected cumulative return. It has been shown that this objective can be represented as an optimization problem of state-action visitation distribution under linea... | 1 |
Pure Message Passing Can Estimate Common Neighbor for Link Prediction Message Passing Neural Networks (MPNNs) have emerged as the *de facto* standard in graph representation learning. However, when it comes to link prediction, they are not always superior to simple heuristics such as Common Neighbor (CN). This discrepa... | 0 |
SAIR: LEARNING SEMANTIC-AWARE IMPLICIT REPRESENTATION Implicit representation of an image can map arbitrary coordinates in the continuous domain to their corresponding color values, presenting a powerful capability for image reconstruction. Nevertheless, existing implicit representation approaches only focus on buildin... | 0 |
Uni3D: Exploring Unified 3D Representation at Scale Scaling up representations for images or text has been extensively investigated in the past few years and has led to revolutions in learning vision and language. However, scalable representation for 3D objects and scenes is relatively unexplored. In this work, we pres... | 1 |
Federated Tuning for Black Box Large Models With the blowout development of pre-trained models (PTMs), the efficient tuning of these models for diverse downstream applications has emerged as a pivotal research concern. Although recent investigations into prompt tuning have provided promising avenues, three salient chal... | 0 |
DS-Prover: A Dynamic Sampling Based Approach for Neural Theorem Proving Theorem proving is a fundamental task in mathematics. With the advent of large language models (LLMs) and interactive theorem provers (ITPs) like Lean, there has been growing interest in integrating LLMs and ITPs to automate theorem proving. In thi... | 0 |
How FaR Are Large Language Models From Agents with Theory-of-Mind? "*Thinking is for Doing.*" Humans can infer other people’s mental states from observations–an ability called Theory-of-Mind (ToM)–and subsequently act pragmatically on those inferences. Existing question answering benchmarks such as ToMi ask models ques... | 0 |
How the Level Sampling Process impacts Zero-Shot Generalisation in Deep Reinforcement Learning A key limitation preventing the wider adoption of autonomous agents trained via deep reinforcement learning (RL) is their limited ability to generalise to new environments, even when these share similar characteristics with e... | 0 |
RGB-Event MOT: A Cross-Modal Benchmark for Multi-Object Tracking Leveraging the power of contemporary deep learning techniques, it has become increasingly convenient for methodologies to recognize, detect, and track objects in real-world scenarios. Nonetheless, challenges persist, particularly regarding the robustness ... | 0 |
Bayesian Vector Optimization with Gaussian Processes Learning problems in which multiple conflicting objectives must be considered simultaneously often arise in various fields, including engineering, drug design, and environmental management. Traditional methods of multi-objective optimization, such as scalarization an... | 0 |
Causal Modelling Agents: Causal Graph Discovery through Synergising Metadata- and Data-driven Reasoning Scientific discovery hinges on the effective integration of metadata, which refers to a set of 'cognitive' operations such as determining what information is relevant for inquiry, and data, which encompasses physical... | 1 |
Anytime Neural Architecture Search on Tabular Data The increasing demand for tabular data analysis calls for transitioning from manual architecture design to Neural Architecture Search (NAS). This transition demands an efficient and responsive anytime NAS approach that is capable of returning current optimal architectu... | 0 |
Debiasing Language Models Using Energy-Guided Ordinary Differential Equations Language Models (LMs) excel in learning from training datasets. However, they often inadvertently incorporate societal biases within the data they draw from, raising fairness concerns in their applications. In response, this paper introduces ... | 0 |
Generalization error bounds for iterative learning algorithms with bounded updates This paper explores the generalization characteristics of iterative learning algorithms with bounded updates for non-convex loss functions, employing information-theoretic techniques. Our key contribution is a novel bound for the general... | 0 |
A Game Theoretic Approach to Meta-Learning: Nash Model-Agnostic Meta-Learning Meta-learning, or learning to learn, aims to develop algorithms that can quickly adapt to new tasks and environments. Model-agnostic meta-learning (MAML), proposed as a bi-level optimization problem, is widely used as a baseline for gradient-... | 0 |
Don't Paint Everyone with the Same Brush: Adaptive Prompt Prototype Learning for Vision-Language Models Vision Language Models (VLMs) have demonstrated great potential on zero-shot classification tasks by computing the similarity between visual and textual embeddings. To adapt VLMs to a downstream task, recent advances... | 0 |
Independent-Set Design of Experiments for Estimating Treatment and Spillover Effects under Network Interference Interference is ubiquitous when conducting causal experiments over networks. Except for certain network structures, causal inference on the network in the presence of interference is difficult due to the enta... | 1 |
Visual Data-Type Understanding does not emerge from scaling Vision-Language Models Recent advances in the development of vision-language models (VLMs) are yielding remarkable success in recognizing visual semantic content, including impressive instances of compositional image understanding. Here, we introduce the novel... | 1 |
Visual Topics via Visual Vocabularies Researchers have long used topic modeling to automatically characterize and summarize text documents without supervision. Can we extract similar structures from collections of images? To do this, we propose *visual vocabularies*, a method to analyze image datasets by decomposing im... | 0 |
Language-Independent Embeddings for Entity Recognition via LLM Data-Level Knowledge Distillation Entity Recognition has always been one of the most important problems in Natural Language Processing. However, there wasn't much research aimed at creating a high-quality Multilingual Domain-Agnostic Foundation Model for En... | 0 |
STREAM: Spatio-TempoRal Evaluation and Analysis Metric for Video Generative Models Image generative models have made significant progress in generating realistic and diverse images, supported by comprehensive guidance from various evaluation metrics. However, current video generative models struggle to generate even
s... | 1 |
FusionAD: Multi-modality Fusion for Prediction and Planning Tasks of Autonomous Driving Building a multi-modality multi-task neural network toward accurate and robust
performance is a de-facto standard in perception task of autonomous driving.
However, leveraging such data from multiple sensors to jointly optimize the ... | 0 |
OrthCaps: An Orthogonal CapsNet with Sparse Attention Routing and Pruning Redundancy is a persistent challenge in Capsule Networks (CapsNet), leading to high computational costs and parameter counts (Jeong et al., 2019; Sharifi et al., 2021; Renzulli & Grangetto, 2022). Although previous works have introduced pruning a... | 0 |
Self-Specialization: Uncovering Latent Expertise within Large Language Models Recent works have demonstrated the effectiveness of self-alignment in which a large language model is, by itself, aligned to follow general instructions through the automatic generation of instructional data using a handful of human-written s... | 0 |
Accelerating Simulation-Based Influence Maximization via Bayesian Optimization Influence Maximization (IM) has garnered significant attention due to its broad applicability in areas such as viral marketing, social network recommendations, and disease containment. The primary goal of IM is to identify an optimal seed se... | 0 |
Sample-aware RandAugment Automatic data augmentation (AutoDA) improves the generalization of neural networks by filling in the missing data in the target distribution. However, mainstream AutoDA methods suffer from either a time-consuming search process that sets barriers for a wide range of applications, or limited pe... | 0 |
Constraining Non-Negative Matrix Factorization to Improve Signature Learning Collaborative filtering approaches are fundamental for learning meaningful low-dimensional representations when only association data is available. Among these methods, Non-negative Matrix Factorization (NMF) has gained prominence due to its c... | 0 |
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