pwc-archive/papers-with-abstracts huggingface dataset:
DELT: A Simple Diversity-driven EarlyLate Training for Dataset Distillation
Authors: Zhiqiang Shen, Ammar Sherif, Zeyuan Yin, Shitong Shao
Date: 2024-11-29 00:00:00
Proceeding: CVPR 2025 1
Tasks: Dataset Distillation, Diversity

Recent advances in dataset distillation have led to solutions in two main directions. The conventional batch-to-batch matching mechanism is ideal for small-scale datasets and includes bi-level optimization methods on models and syntheses, such as FRePo, RCIG, and RaT-BPTT, as well as other methods like distribution matching, gradient matching, and weight trajectory matching. Conversely, batch-to-global matching typifies decoupled methods, which are particularly advantageous for large-scale datasets. This approach has garnered substantial interest within the community, as seen in SRe$^2$L, G-VBSM, WMDD, and CDA. A primary challenge with the second approach is the lack of diversity among syntheses within each class since samples are optimized independently and the same global supervision signals are reused across different synthetic images. In this study, we propose a new Diversity-driven EarlyLate Training (DELT) scheme to enhance the diversity of images in batch-to-global matching with less computation. Our approach is conceptually simple yet effective, it partitions predefined IPC samples into smaller subtasks and employs local optimizations to distill each subset into distributions from distinct phases, reducing the uniformity induced by the unified optimization process. These distilled images from the subtasks demonstrate effective generalization when applied to the entire task. We conduct extensive experiments on CIFAR, Tiny-ImageNet, ImageNet-1K, and its sub-datasets. Our approach outperforms the previous state-of-the-art by 2$\sim$5% on average across different datasets and IPCs (images per class), increasing diversity per class by more than 5% while reducing synthesis time by up to 39.3% for enhancing the training efficiency. Code is available at: https://github.com/VILA-Lab/DELT.
Critical Tokens Matter: Token-Level Contrastive Estimation Enhances LLM's Reasoning Capability
Authors: Zicheng Lin, Tian Liang, Jiahao Xu, Qiuzhi Lin, Xing Wang, Ruilin Luo, Chufan Shi, Siheng Li, Yujiu Yang, Zhaopeng Tu
Date: 2024-11-29 00:00:00
Tasks: GSM8K, Math, Mathematical Reasoning

Mathematical reasoning tasks pose significant challenges for large language models (LLMs) because they require precise logical deduction and sequence analysis. In this work, we introduce the concept of critical tokens -- elements within reasoning trajectories that significantly influence incorrect outcomes. We present a novel framework for identifying these tokens through rollout sampling and demonstrate their substantial divergence from traditional error tokens. Through extensive experiments on datasets such as GSM8K and MATH500, we show that identifying and replacing critical tokens significantly improves model accuracy. We propose an efficient methodology for pinpointing these tokens in large-scale datasets using contrastive estimation and extend this framework to enhance model training processes with direct preference optimization (DPO). Experimental results on GSM8K and MATH500 benchmarks with the widely used models Llama-3 (8B and 70B) and Deepseek-math (7B) demonstrate the effectiveness of the proposed approach, cDPO. Our results underscore the potential of leveraging critical tokens to reduce errors in reasoning tasks, advancing the development of AI systems capable of robust logical deduction. Our code, annotated datasets, and trained models are available at https://github.com/chenzhiling9954/Critical-Tokens-Matter to support and encourage future research in this promising field.
VLSBench: Unveiling Visual Leakage in Multimodal Safety
Authors: Xuhao Hu, Dongrui Liu, Hao Li, Xuanjing Huang, Jing Shao
Date: 2024-11-29 00:00:00

Safety concerns of Multimodal large language models (MLLMs) have gradually become an important problem in various applications. Surprisingly, previous works indicate a counterintuitive phenomenon that using textual unlearning to align MLLMs achieves comparable safety performances with MLLMs aligned with image text pairs. To explain such a phenomenon, we discover a Visual Safety Information Leakage (VSIL) problem in existing multimodal safety benchmarks, i.e., the potentially risky content in the image has been revealed in the textual query. Thus, MLLMs can easily refuse these sensitive image-text pairs according to textual queries only, leading to unreliable cross-modality safety evaluation of MLLMs. We also conduct a further comparison experiment between textual alignment and multimodal alignment to highlight this drawback. To this end, we construct multimodal Visual Leakless Safety Bench (VLSBench) with 2.2k image-text pairs through an automated data pipeline. Experimental results indicate that VLSBench poses a significant challenge to both open-source and close-source MLLMs, e.g., LLaVA, Qwen2-VL and GPT-4o. Besides, we empirically compare textual and multimodal alignment methods on VLSBench and find that textual alignment is effective enough for multimodal safety scenarios with VSIL, while multimodal alignment is preferable for safety scenarios without VSIL. Code and data are released under https://github.com/AI45Lab/VLSBench
Dynamic EEG-fMRI mapping: Revealing the relationship between brain connectivity and cognitive state
Authors: Guiran Liu, Binrong Zhu
Date: 2024-11-29 00:00:00
Tasks: EEG, Functional Connectivity

This study investigated the dynamic connectivity patterns between EEG and fMRI modalities, contributing to our understanding of brain network interactions. By employing a comprehensive approach that integrated static and dynamic analyses of EEG-fMRI data, we were able to uncover distinct connectivity states and characterize their temporal fluctuations. The results revealed modular organization within the intrinsic connectivity networks (ICNs) of the brain, highlighting the significant roles of sensory systems and the default mode network. The use of a sliding window technique allowed us to assess how functional connectivity varies over time, further elucidating the transient nature of brain connectivity. Additionally, our findings align with previous literature, reinforcing the notion that cognitive states can be effectively identified through short-duration data, specifically within the 30-60 second timeframe. The established relationships between connectivity strength and cognitive processes, particularly during different visual states, underscore the relevance of our approach for future research into brain dynamics. Overall, this study not only enhances our understanding of the interplay between EEG and fMRI signals but also paves the way for further exploration into the neural correlates of cognitive functions and their implications in clinical settings. Future research should focus on refining these methodologies and exploring their applications in various cognitive and clinical contexts.
SIMS: Simulating Stylized Human-Scene Interactions with Retrieval-Augmented Script Generation
Authors: Wenjia Wang, Liang Pan, Zhiyang Dou, Jidong Mei, Zhouyingcheng Liao, Yuke Lou, Yifan Wu, Lei Yang, Jingbo Wang, Taku Komura
Date: 2024-11-29 00:00:00
Tasks: Motion Planning, RAG, Retrieval, Retrieval-augmented Generation, Scene Understanding, Script Generation

Simulating stylized human-scene interactions (HSI) in physical environments is a challenging yet fascinating task. Prior works emphasize long-term execution but fall short in achieving both diverse style and physical plausibility. To tackle this challenge, we introduce a novel hierarchical framework named SIMS that seamlessly bridges highlevel script-driven intent with a low-level control policy, enabling more expressive and diverse human-scene interactions. Specifically, we employ Large Language Models with Retrieval-Augmented Generation (RAG) to generate coherent and diverse long-form scripts, providing a rich foundation for motion planning. A versatile multicondition physics-based control policy is also developed, which leverages text embeddings from the generated scripts to encode stylistic cues, simultaneously perceiving environmental geometries and accomplishing task goals. By integrating the retrieval-augmented script generation with the multi-condition controller, our approach provides a unified solution for generating stylized HSI motions. We further introduce a comprehensive planning dataset produced by RAG and a stylized motion dataset featuring diverse locomotions and interactions. Extensive experiments demonstrate SIMS's effectiveness in executing various tasks and generalizing across different scenarios, significantly outperforming previous methods.
Handling irresolvable conflicts in the Semantic Web: an RDF-based conflict-tolerant version of the Deontic Traditional Scheme
Authors: Livio Robaldo, Gianluca Pozzato
Date: 2024-11-29 00:00:00

This paper presents a new ontology that implements the well-known Deontic Traditional Scheme in RDFs and SPARQL, fit to handle irresolvable conflicts, i.e., situations in which two or more statements prescribe conflicting obligations, prohibitions, or permissions, with none of them being "stronger" than the other one(s). In our view, this paper marks a significant advancement in standard theoretical research in formal Deontic Logic. Most contemporary approaches in this field are confined to the propositional level, mainly focus on the notion of obligation, and lack implementations. The proposed framework is encoded in RDF, which is not only a first-order language but also the most widely used knowledge representation language, as it forms the foundation of the Semantic Web. Moreover, the proposed computational ontology formalizes all deontic modalities defined in the Deontic Traditional Scheme, without specifically focusing on obligations, and offers constructs to model and reason with various types of irresolvable conflicts, violations, and the interaction between deontic modalities and contextual constraints in a given state of affairs. To the best of our knowledge, no existing approach in the literature addresses all these aspects within a unified integrated framework. All examples presented and discussed in this paper, together with Java code and clear instructions to re-execute them locally, are available at https://github.com/liviorobaldo/conflict-tolerantDeonticTraditionalScheme
Quantifying the synthetic and real domain gap in aerial scene understanding
Authors: Alina Marcu
Date: 2024-11-29 00:00:00
Tasks: Domain Adaptation, Scene Understanding

Quantifying the gap between synthetic and real-world imagery is essential for improving both transformer-based models - that rely on large volumes of data - and datasets, especially in underexplored domains like aerial scene understanding where the potential impact is significant. This paper introduces a novel methodology for scene complexity assessment using Multi-Model Consensus Metric (MMCM) and depth-based structural metrics, enabling a robust evaluation of perceptual and structural disparities between domains. Our experimental analysis, utilizing real-world (Dronescapes) and synthetic (Skyscenes) datasets, demonstrates that real-world scenes generally exhibit higher consensus among state-of-the-art vision transformers, while synthetic scenes show greater variability and challenge model adaptability. The results underline the inherent complexities and domain gaps, emphasizing the need for enhanced simulation fidelity and model generalization. This work provides critical insights into the interplay between domain characteristics and model performance, offering a pathway for improved domain adaptation strategies in aerial scene understanding.
PDDLFuse: A Tool for Generating Diverse Planning Domains
Authors: Vedant Khandelwal, Amit Sheth, Forest Agostinelli
Date: 2024-11-29 00:00:00

Various real-world challenges require planning algorithms that can adapt to a broad range of domains. Traditionally, the creation of planning domains has relied heavily on human implementation, which limits the scale and diversity of available domains. While recent advancements have leveraged generative AI technologies such as large language models (LLMs) for domain creation, these efforts have predominantly focused on translating existing domains from natural language descriptions rather than generating novel ones. In contrast, the concept of domain randomization, which has been highly effective in reinforcement learning, enhances performance and generalizability by training on a diverse array of randomized new domains. Inspired by this success, our tool, PDDLFuse, aims to bridge this gap in Planning Domain Definition Language (PDDL). PDDLFuse is designed to generate new, diverse planning domains that can be used to validate new planners or test foundational planning models. We have developed methods to adjust the domain generators parameters to modulate the difficulty of the domains it generates. This adaptability is crucial as existing domain-independent planners often struggle with more complex problems. Initial tests indicate that PDDLFuse efficiently creates intricate and varied domains, representing a significant advancement over traditional domain generation methods and making a contribution towards planning research.
LUMIA: Linear probing for Unimodal and MultiModal Membership Inference Attacks leveraging internal LLM states
Authors: Luis Ibanez-Lissen, Lorena Gonzalez-Manzano, Jose Maria de Fuentes, Nicolas Anciaux, Joaquin Garcia-Alfaro
Date: 2024-11-29 00:00:00

Large Language Models (LLMs) are increasingly used in a variety of applications, but concerns around membership inference have grown in parallel. Previous efforts focus on black-to-grey-box models, thus neglecting the potential benefit from internal LLM information. To address this, we propose the use of Linear Probes (LPs) as a method to detect Membership Inference Attacks (MIAs) by examining internal activations of LLMs. Our approach, dubbed LUMIA, applies LPs layer-by-layer to get fine-grained data on the model inner workings. We test this method across several model architectures, sizes and datasets, including unimodal and multimodal tasks. In unimodal MIA, LUMIA achieves an average gain of 15.71 % in Area Under the Curve (AUC) over previous techniques. Remarkably, LUMIA reaches AUC>60% in 65.33% of cases -- an increment of 46.80% against the state of the art. Furthermore, our approach reveals key insights, such as the model layers where MIAs are most detectable. In multimodal models, LPs indicate that visual inputs can significantly contribute to detect MIAs -- AUC>60% is reached in 85.90% of experiments.
Enhanced anomaly detection in well log data through the application of ensemble GANs
Authors: Abdulrahman Al-Fakih, A. Koeshidayatullah, Tapan Mukerji, SanLinn I. Kaka
Date: 2024-11-29 00:00:00
Tasks: Anomaly Detection

Although generative adversarial networks (GANs) have shown significant success in modeling data distributions for image datasets, their application to structured or tabular data, such as well logs, remains relatively underexplored. This study extends the ensemble GANs (EGANs) framework to capture the distribution of well log data and detect anomalies that fall outside of these distributions. The proposed approach compares the performance of traditional methods, such as Gaussian mixture models (GMMs), with EGANs in detecting anomalies outside the expected data distributions. For the gamma ray (GR) dataset, EGANs achieved a precision of 0.62 and F1 score of 0.76, outperforming GMM's precision of 0.38 and F1 score of 0.54. Similarly, for travel time (DT), EGANs achieved a precision of 0.70 and F1 score of 0.79, surpassing GMM 0.56 and 0.71. In the neutron porosity (NPHI) dataset, EGANs recorded a precision of 0.53 and F1 score of 0.68, outshining GMM 0.47 and 0.61. For the bulk density (RHOB) dataset, EGANs achieved a precision of 0.52 and an F1 score of 0.67, slightly outperforming GMM, which yielded a precision of 0.50 and an F1 score of 0.65. This work's novelty lies in applying EGANs for well log data analysis, showcasing their ability to learn data patterns and identify anomalies that deviate from them. This approach offers more reliable anomaly detection compared to traditional methods like GMM. The findings highlight the potential of EGANs in enhancing anomaly detection for well log data, delivering significant implications for optimizing drilling strategies and reservoir management through more accurate, data-driven insights into subsurface characterization.
DeMo: Decoupled Momentum Optimization
Authors: Bowen Peng, Jeffrey Quesnelle, Diederik P. Kingma
Date: 2024-11-29 00:00:00
Tasks: 10-shot image generation, 1 Image, 2*2 Stitchi

Training large neural networks typically requires sharing gradients between accelerators through specialized high-speed interconnects. Drawing from the signal processing principles of frequency decomposition and energy compaction, we demonstrate that synchronizing full optimizer states and model parameters during training is unnecessary. By decoupling momentum updates and allowing controlled divergence in optimizer states across accelerators, we achieve improved convergence compared to state-of-the-art optimizers. We introduce {\textbf{De}}coupled {\textbf{Mo}}mentum (DeMo), a fused optimizer and data parallel algorithm that reduces inter-accelerator communication requirements by several orders of magnitude. This enables training of large neural networks even with limited network bandwidth and heterogeneous hardware. Our method is topology-agnostic and architecture-independent and supports scalable clock-synchronous distributed training with negligible compute and memory overhead. Empirical results show that models trained with DeMo match or exceed the performance of equivalent models trained with AdamW, while eliminating the need for high-speed interconnects when pre-training large scale foundation models. An open source reference PyTorch implementation is published on GitHub at https://github.com/bloc97/DeMo
Reverse Thinking Makes LLMs Stronger Reasoners
Authors: Justin Chih-Yao Chen, Zifeng Wang, Hamid Palangi, Rujun Han, Sayna Ebrahimi, Long Le, Vincent Perot, Swaroop Mishra, Mohit Bansal, Chen-Yu Lee, Tomas Pfister
Date: 2024-11-29 00:00:00
Tasks: Data Augmentation, Knowledge Distillation, Logical Reasoning, Math, Multi-Task Learning

Reverse thinking plays a crucial role in human reasoning. Humans can reason not only from a problem to a solution but also in reverse, i.e., start from the solution and reason towards the problem. This often enhances overall reasoning performance as it enables consistency checks between their forward and backward thinking. To enable Large Language Models (LLMs) to perform reverse thinking, we introduce Reverse-Enhanced Thinking (RevThink), a framework composed of data augmentation and learning objectives. In RevThink, we augment the dataset by collecting structured forward-backward reasoning from a teacher model, consisting of: (1) the original question, (2) forward reasoning, (3) backward question, and (4) backward reasoning. We then employ three objectives to train a smaller student model in a multi-task learning fashion: (a) generate forward reasoning from a question, (b) generate a backward question from a question, and (c) generate backward reasoning from the backward question. Experiments across 12 datasets covering commonsense, math, and logical reasoning show an average 13.53% improvement over the student model's zero-shot performance and a 6.84% improvement over the strongest knowledge distillation baselines. Moreover, our method demonstrates sample efficiency -- using only 10% of the correct forward reasoning from the training data, it outperforms a standard fine-tuning method trained on 10x more forward reasoning. RevThink also exhibits strong generalization to out-of-distribution held-out datasets.
Scaling Transformers for Low-Bitrate High-Quality Speech Coding
Authors: Julian D Parker, Anton Smirnov, Jordi Pons, CJ Carr, Zack Zukowski, Zach Evans, Xubo Liu
Date: 2024-11-29 00:00:00
Tasks: Quantization

The tokenization of speech with neural audio codec models is a vital part of modern AI pipelines for the generation or understanding of speech, alone or in a multimodal context. Traditionally such tokenization models have concentrated on low parameter-count architectures using only components with strong inductive biases. In this work we show that by scaling a transformer architecture with large parameter count to this problem, and applying a flexible Finite Scalar Quantization (FSQ) based bottleneck, it is possible to reach state-of-the-art speech quality at extremely low bit-rates of $400$ or $700$ bits-per-second. The trained models strongly out-perform existing baselines in both objective and subjective tests.
Q-learning-based Model-free Safety Filter
Authors: Guo Ning Sue, Yogita Choudhary, Richard Desatnik, Carmel Majidi, John Dolan, Guanya Shi
Date: 2024-11-29 00:00:00
Tasks: model, Q-Learning

Ensuring safety via safety filters in real-world robotics presents significant challenges, particularly when the system dynamics is complex or unavailable. To handle this issue, learning-based safety filters recently gained popularity, which can be classified as model-based and model-free methods. Existing model-based approaches requires various assumptions on system model (e.g., control-affine), which limits their application in complex systems, and existing model-free approaches need substantial modifications to standard RL algorithms and lack versatility. This paper proposes a simple, plugin-and-play, and effective model-free safety filter learning framework. We introduce a novel reward formulation and use Q-learning to learn Q-value functions to safeguard arbitrary task specific nominal policies via filtering out their potentially unsafe actions. The threshold used in the filtering process is supported by our theoretical analysis. Due to its model-free nature and simplicity, our framework can be seamlessly integrated with various RL algorithms. We validate the proposed approach through simulations on double integrator and Dubin's car systems and demonstrate its effectiveness in real-world experiments with a soft robotic limb.
Zero-shot Musical Stem Retrieval with Joint-Embedding Predictive Architectures
Authors: Alain Riou, Antonin Gagneré, Gaëtan Hadjeres, Stefan Lattner, Geoffroy Peeters
Date: 2024-11-29 00:00:00
Tasks: Beat Tracking, Contrastive Learning, Retrieval

In this paper, we tackle the task of musical stem retrieval. Given a musical mix, it consists in retrieving a stem that would fit with it, i.e., that would sound pleasant if played together. To do so, we introduce a new method based on Joint-Embedding Predictive Architectures, where an encoder and a predictor are jointly trained to produce latent representations of a context and predict latent representations of a target. In particular, we design our predictor to be conditioned on arbitrary instruments, enabling our model to perform zero-shot stem retrieval. In addition, we discover that pretraining the encoder using contrastive learning drastically improves the model's performance. We validate the retrieval performances of our model using the MUSDB18 and MoisesDB datasets. We show that it significantly outperforms previous baselines on both datasets, showcasing its ability to support more or less precise (and possibly unseen) conditioning. We also evaluate the learned embeddings on a beat tracking task, demonstrating that they retain temporal structure and local information.