id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2412.17023 | Parameter-Efficient Interventions for Enhanced Model Merging | [
"cs.CV"
] | Model merging combines knowledge from task-specific models into a unified multi-task model to avoid joint training on all task data. However, current methods face challenges due to representation bias, which can interfere with tasks performance. As a remedy, we propose IntervMerge, a novel approach to multi-task model ... | {
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2412.17029 | GraphAgent: Agentic Graph Language Assistant | [
"cs.AI"
] | Real-world data is represented in both structured (e.g., graph connections) and unstructured (e.g., textual, visual information) formats, encompassing complex relationships that include explicit links (such as social connections and user behaviors) and implicit interdependencies among semantic entities, often illustrat... | {
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2412.17031 | A Reality Check on Context Utilisation for Retrieval-Augmented
Generation | [
"cs.CL",
"cs.AI"
] | Retrieval-augmented generation (RAG) helps address the limitations of the parametric knowledge embedded within a language model (LM). However, investigations of how LMs utilise retrieved information of varying complexity in real-world scenarios have been limited to synthetic contexts. We introduce DRUID (Dataset of Ret... | {
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2412.17032 | MINTQA: A Multi-Hop Question Answering Benchmark for Evaluating LLMs on
New and Tail Knowledge | [
"cs.CL"
] | Large language models (LLMs) have demonstrated impressive capabilities in various reasoning tasks but face significant challenges with complex, knowledge-intensive multi-hop queries, particularly those involving new or long-tail knowledge. Existing benchmarks often fail to fully address these challenges. To bridge this... | {
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2412.17034 | Shaping the Safety Boundaries: Understanding and Defending Against
Jailbreaks in Large Language Models | [
"cs.CL"
] | Jailbreaking in Large Language Models (LLMs) is a major security concern as it can deceive LLMs to generate harmful text. Yet, there is still insufficient understanding of how jailbreaking works, which makes it hard to develop effective defense strategies. We aim to shed more light into this issue: we conduct a detaile... | {
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2412.17038 | ErasableMask: A Robust and Erasable Privacy Protection Scheme against
Black-box Face Recognition Models | [
"cs.CV",
"cs.AI"
] | While face recognition (FR) models have brought remarkable convenience in face verification and identification, they also pose substantial privacy risks to the public. Existing facial privacy protection schemes usually adopt adversarial examples to disrupt face verification of FR models. However, these schemes often su... | {
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2412.17040 | HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth
by Learning Weight Trajectories | [
"cs.LG"
] | To efficiently adapt large models or to train generative models of neural representations, Hypernetworks have drawn interest. While hypernetworks work well, training them is cumbersome, and often requires ground truth optimized weights for each sample. However, obtaining each of these weights is a training problem of i... | {
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2412.17041 | An OpenMind for 3D medical vision self-supervised learning | [
"cs.CV",
"cs.AI",
"cs.LG",
"eess.IV"
] | The field of 3D medical vision self-supervised learning lacks consistency and standardization. While many methods have been developed it is impossible to identify the current state-of-the-art, due to i) varying and small pre-training datasets, ii) varying architectures, and iii) being evaluated on differing downstream ... | {
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2412.17042 | Adapting Image-to-Video Diffusion Models for Large-Motion Frame
Interpolation | [
"cs.CV"
] | With the development of video generation models has advanced significantly in recent years, we adopt large-scale image-to-video diffusion models for video frame interpolation. We present a conditional encoder designed to adapt an image-to-video model for large-motion frame interpolation. To enhance performance, we inte... | {
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2412.17048 | Why Do Speech Language Models Fail to Generate Semantically Coherent
Outputs? A Modality Evolving Perspective | [
"eess.AS",
"cs.CL",
"cs.SD"
] | Although text-based large language models exhibit human-level writing ability and remarkable intelligence, speech language models (SLMs) still struggle to generate semantically coherent outputs. There are several potential reasons for this performance degradation: (A) speech tokens mainly provide phonetic information r... | {
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2412.17049 | Modular Conversational Agents for Surveys and Interviews | [
"cs.HC",
"cs.CL",
"cs.CY",
"cs.MM"
] | Surveys and interviews (structured, semi-structured, or unstructured) are widely used for collecting insights on emerging or hypothetical scenarios. Traditional human-led methods often face challenges related to cost, scalability, and consistency. Recently, various domains have begun to explore the use of conversationa... | {
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2412.17052 | VilBias: A Study of Bias Detection through Linguistic and Visual Cues ,
presenting Annotation Strategies, Evaluation, and Key Challenges | [
"cs.AI"
] | The integration of Large Language Models (LLMs) and Vision-Language Models (VLMs) opens new avenues for addressing complex challenges in multimodal content analysis, particularly in biased news detection. This study introduces VLBias, a framework that leverages state-of-the-art LLMs and VLMs to detect linguistic and vi... | {
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2412.17053 | DR-Encoder: Encode Low-rank Gradients with Random Prior for Large
Language Models Differentially Privately | [
"cs.LG",
"cs.AI",
"cs.CR"
] | The emergence of the Large Language Model (LLM) has shown their superiority in a wide range of disciplines, including language understanding and translation, relational logic reasoning, and even partial differential equations solving. The transformer is the pervasive backbone architecture for the foundation model const... | {
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2412.17054 | Differentially Private Random Block Coordinate Descent | [
"math.OC",
"cs.CR",
"cs.LG",
"stat.ML"
] | Coordinate Descent (CD) methods have gained significant attention in machine learning due to their effectiveness in solving high-dimensional problems and their ability to decompose complex optimization tasks. However, classical CD methods were neither designed nor analyzed with data privacy in mind, a critical concern ... | {
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2412.17056 | The HalluRAG Dataset: Detecting Closed-Domain Hallucinations in RAG
Applications Using an LLM's Internal States | [
"cs.CL",
"cs.LG"
] | Detecting hallucinations in large language models (LLMs) is critical for enhancing their reliability and trustworthiness. Most research focuses on hallucinations as deviations from information seen during training. However, the opaque nature of an LLM's parametric knowledge complicates the understanding of why generate... | {
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2412.17061 | Multi-Agent Sampling: Scaling Inference Compute for Data Synthesis with
Tree Search-Based Agentic Collaboration | [
"cs.CL"
] | Scaling laws for inference compute in multi-agent systems remain under-explored compared to single-agent scenarios. This work aims to bridge this gap by investigating the problem of data synthesis through multi-agent sampling, where synthetic responses are generated by sampling from multiple distinct language models. E... | {
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2412.17062 | Hybrid Beamforming Design for RSMA-enabled Near-Field Integrated Sensing
and Communications | [
"cs.IT",
"eess.SP",
"math.IT"
] | To enable high data rates and sensing resolutions, integrated sensing and communication (ISAC) networks leverage extremely large antenna arrays and high frequencies, extending the Rayleigh distance and making near-field (NF) spherical wave propagation dominant. This unlocks numerous spatial degrees of freedom, raising ... | {
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2412.17063 | Computational Analysis of Character Development in Holocaust Testimonies | [
"cs.CL"
] | This work presents a computational approach to analyze character development along the narrative timeline. The analysis characterizes the inner and outer changes the protagonist undergoes within a narrative, and the interplay between them. We consider transcripts of Holocaust survivor testimonies as a test case, each t... | {
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2412.17066 | Interactive Classification Metrics: A graphical application to build
robust intuition for classification model evaluation | [
"cs.LG",
"stat.ML"
] | Machine learning continues to grow in popularity in academia, in industry, and is increasingly used in other fields. However, most of the common metrics used to evaluate even simple binary classification models have shortcomings that are neither immediately obvious nor consistently taught to practitioners. Here we pres... | {
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2412.17067 | Circular-shift-based Vector Linear Network Coding and Its Application to
Array Codes | [
"cs.IT",
"math.IT"
] | Circular-shift linear network coding (LNC) is a class of vector LNC with local encoding kernels selected from cyclic permutation matrices, so that it has low coding complexities. However, it is insufficient to exactly achieve the capacity of a multicast network, so the data units transmitted along the network need to c... | {
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2412.17068 | A Plug-and-Play Natural Language Rewriter for Natural Language to SQL | [
"cs.DB"
] | Existing Natural Language to SQL (NL2SQL) solutions have made significant advancements, yet challenges persist in interpreting and translating NL queries, primarily due to users' limited understanding of database schemas or memory biases toward specific table or column values. These challenges often result in incorrect... | {
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2412.17069 | Optimizing Data Curation through Spectral Analysis and Joint Batch
Selection (SALN) | [
"cs.LG",
"cs.AI"
] | In modern deep learning models, long training times and large datasets present significant challenges to both efficiency and scalability. Effective data curation and sample selection are crucial for optimizing the training process of deep neural networks. This paper introduces SALN, a method designed to prioritize and ... | {
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2412.17075 | Iterative NLP Query Refinement for Enhancing Domain-Specific Information
Retrieval: A Case Study in Career Services | [
"cs.IR",
"cs.CL"
] | Retrieving semantically relevant documents in niche domains poses significant challenges for traditional TF-IDF-based systems, often resulting in low similarity scores and suboptimal retrieval performance. This paper addresses these challenges by introducing an iterative and semi-automated query refinement methodology ... | {
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2412.17077 | SubstationAI: Multimodal Large Model-Based Approaches for Analyzing
Substation Equipment Faults | [
"cs.AI"
] | The reliability of substation equipment is crucial to the stability of power systems, but traditional fault analysis methods heavily rely on manual expertise, limiting their effectiveness in handling complex and large-scale data. This paper proposes a substation equipment fault analysis method based on a multimodal lar... | {
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2412.17080 | Aligning Graphical and Functional Causal Abstractions | [
"cs.AI"
] | Causal abstractions allow us to relate causal models on different levels of granularity. To ensure that the models agree on cause and effect, frameworks for causal abstractions define notions of consistency. Two distinct methods for causal abstraction are common in the literature: (i) graphical abstractions, such as Cl... | {
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2412.17082 | MARINA-P: Superior Performance in Non-smooth Federated Optimization with
Adaptive Stepsizes | [
"cs.LG",
"math.OC",
"stat.ML"
] | Non-smooth communication-efficient federated optimization is crucial for many machine learning applications, yet remains largely unexplored theoretically. Recent advancements have primarily focused on smooth convex and non-convex regimes, leaving a significant gap in understanding the non-smooth convex setting. Additio... | {
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2412.17092 | SAIL: Sample-Centric In-Context Learning for Document Information
Extraction | [
"cs.CL",
"cs.AI"
] | Document Information Extraction (DIE) aims to extract structured information from Visually Rich Documents (VRDs). Previous full-training approaches have demonstrated strong performance but may struggle with generalization to unseen data. In contrast, training-free methods leverage powerful pre-trained models like Large... | {
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2412.17094 | Analysis on LLMs Performance for Code Summarization | [
"cs.SE",
"cs.AI"
] | Code summarization aims to generate concise natural language descriptions for source code. Deep learning has been used more and more recently in software engineering, particularly for tasks like code creation and summarization. Specifically, it appears that the most current Large Language Models with coding perform wel... | {
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2412.17098 | DreamOmni: Unified Image Generation and Editing | [
"cs.CV"
] | Currently, the success of large language models (LLMs) illustrates that a unified multitasking approach can significantly enhance model usability, streamline deployment, and foster synergistic benefits across different tasks. However, in computer vision, while text-to-image (T2I) models have significantly improved gene... | {
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2412.17104 | A diversity-enhanced genetic algorithm for efficient exploration of
parameter spaces | [
"cs.NE",
"hep-ph"
] | We present a Python package together with a practical guide for the implementation of a lightweight diversity-enhanced genetic algorithm (GA) approach for the exploration of multi-dimensional parameter spaces. Searching a parameter space for regions with desirable properties, e.g. compatibility with experimental data, ... | {
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2412.17105 | Refining CNN-based Heatmap Regression with Gradient-based Corner Points
for Electrode Localization | [
"cs.CV"
] | We propose a method for detecting the electrode positions in lithium-ion batteries. The process begins by identifying the region of interest (ROI) in the battery's X-ray image through corner point detection. A convolutional neural network is then used to regress the pole positions within this ROI. Finally, the regresse... | {
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2412.17107 | Grams: Gradient Descent with Adaptive Momentum Scaling | [
"cs.LG",
"cs.AI",
"cs.DS",
"math.OC"
] | We introduce \textbf{Gr}adient Descent with \textbf{A}daptive \textbf{M}omentum \textbf{S}caling (\textbf{Grams}), a novel optimization algorithm that decouples the direction and magnitude of parameter updates in deep learning. Unlike traditional optimizers that directly integrate momentum into updates, Grams separates... | {
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2412.17109 | Similarity Trajectories: Linking Sampling Process to Artifacts in
Diffusion-Generated Images | [
"cs.CV",
"cs.LG"
] | Artifact detection algorithms are crucial to correcting the output generated by diffusion models. However, because of the variety of artifact forms, existing methods require substantial annotated data for training. This requirement limits their scalability and efficiency, which restricts their wide application. This pa... | {
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2412.17110 | Deep Joint Source Channel Coding for Secure End-to-End Image
Transmission | [
"cs.IT",
"eess.SP",
"math.IT"
] | Deep neural network (DNN)-based joint source and channel coding is proposed for end-to-end secure image transmission against multiple eavesdroppers. Both scenarios of colluding and non-colluding eavesdroppers are considered. Instead of idealistic assumptions of perfectly known and i.i.d. source and channel distribution... | {
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2412.17111 | Learning to Adapt to Low-Resource Paraphrase Generation | [
"cs.CL"
] | Paraphrase generation is a longstanding NLP task and achieves great success with the aid of large corpora. However, transferring a paraphrasing model to another domain encounters the problem of domain shifting especially when the data is sparse. At the same time, widely using large pre-trained language models (PLMs) fa... | {
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2412.17113 | Adam on Local Time: Addressing Nonstationarity in RL with Relative Adam
Timesteps | [
"cs.LG",
"math.OC"
] | In reinforcement learning (RL), it is common to apply techniques used broadly in machine learning such as neural network function approximators and momentum-based optimizers. However, such tools were largely developed for supervised learning rather than nonstationary RL, leading practitioners to adopt target networks, ... | {
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2412.17114 | Decentralized Governance of Autonomous AI Agents | [
"cs.AI",
"cs.ET"
] | Autonomous AI agents present transformative opportunities and significant governance challenges. Existing frameworks, such as the EU AI Act and the NIST AI Risk Management Framework, fall short of addressing the complexities of these agents, which are capable of independent decision-making, learning, and adaptation. To... | {
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2412.17116 | Fair and Accurate Regression: Strong Formulations and Algorithms | [
"cs.LG",
"cs.CY",
"math.OC",
"stat.ML"
] | This paper introduces mixed-integer optimization methods to solve regression problems that incorporate fairness metrics. We propose an exact formulation for training fair regression models. To tackle this computationally hard problem, we study the polynomially-solvable single-factor and single-observation subproblems a... | {
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2412.17118 | Transformer-Based Model Predictive Path Integral Control | [
"cs.RO",
"cs.SY",
"eess.SY"
] | This paper presents a novel approach to improve the Model Predictive Path Integral (MPPI) control by using a transformer to initialize the mean control sequence. Traditional MPPI methods often struggle with sample efficiency and computational costs due to suboptimal initial rollouts. We propose TransformerMPPI, which u... | {
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2412.17119 | Empirical Coordination of Separable Quantum Correlations | [
"quant-ph",
"cs.IT",
"math.IT"
] | We introduce the notion of empirical coordination for quantum correlations. Quantum mechanics enables the calculation of probabilities for experimental outcomes, emphasizing statistical averages rather than detailed descriptions of individual events. Empirical coordination is thus a natural framework for quantum system... | {
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2412.17121 | Scalable Speech Enhancement with Dynamic Channel Pruning | [
"eess.AS",
"cs.LG",
"cs.SD"
] | Speech Enhancement (SE) is essential for improving productivity in remote collaborative environments. Although deep learning models are highly effective at SE, their computational demands make them impractical for embedded systems. Furthermore, acoustic conditions can change significantly in terms of difficulty, wherea... | {
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2412.17123 | Fairness in Reinforcement Learning with Bisimulation Metrics | [
"cs.LG",
"cs.CY"
] | Ensuring long-term fairness is crucial when developing automated decision making systems, specifically in dynamic and sequential environments. By maximizing their reward without consideration of fairness, AI agents can introduce disparities in their treatment of groups or individuals. In this paper, we establish the co... | {
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2412.17128 | Lies, Damned Lies, and Distributional Language Statistics: Persuasion
and Deception with Large Language Models | [
"cs.CL",
"cs.CY",
"cs.HC"
] | Large Language Models (LLMs) can generate content that is as persuasive as human-written text and appear capable of selectively producing deceptive outputs. These capabilities raise concerns about potential misuse and unintended consequences as these systems become more widely deployed. This review synthesizes recent e... | {
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2412.17131 | LLMsAgainstHate @ NLU of Devanagari Script Languages 2025: Hate Speech
Detection and Target Identification in Devanagari Languages via Parameter
Efficient Fine-Tuning of LLMs | [
"cs.CL"
] | The detection of hate speech has become increasingly important in combating online hostility and its real-world consequences. Despite recent advancements, there is limited research addressing hate speech detection in Devanagari-scripted languages, where resources and tools are scarce. While large language models (LLMs)... | {
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2412.17136 | Empirical evaluation of normalizing flows in Markov Chain Monte Carlo | [
"cs.LG",
"stat.CO",
"stat.ML"
] | Recent advances in MCMC use normalizing flows to precondition target distributions and enable jumps to distant regions. However, there is currently no systematic comparison of different normalizing flow architectures for MCMC. As such, many works choose simple flow architectures that are readily available and do not co... | {
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2412.17139 | Style Transfer Dataset: What Makes A Good Stylization? | [
"cs.CV",
"eess.IV"
] | We present a new dataset with the goal of advancing image style transfer - the task of rendering one image in the style of another image. The dataset covers various content and style images of different size and contains 10.000 stylizations manually rated by three annotators in 1-10 scale. Based on obtained ratings, we... | {
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2412.17142 | AI-Based Teat Shape and Skin Condition Prediction for Dairy Management | [
"cs.AI"
] | Dairy owners spend significant effort to keep their animals healthy. There is good reason to hope that technologies such as computer vision and artificial intelligence (AI) could reduce these costs, yet obstacles arise when adapting advanced tools to farming environments. In this work, we adapt AI tools to dairy cow te... | {
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2412.17143 | ASP-based Multi-shot Reasoning via DLV2 with Incremental Grounding | [
"cs.AI"
] | DLV2 is an AI tool for Knowledge Representation and Reasoning which supports Answer Set Programming (ASP) - a logic-based declarative formalism, successfully used in both academic and industrial applications. Given a logic program modelling a computational problem, an execution of DLV2 produces the so-called answer set... | {
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2412.17146 | LLM Agent for Fire Dynamics Simulations | [
"cs.AI",
"physics.flu-dyn"
] | Significant advances have been achieved in leveraging foundation models, such as large language models (LLMs), to accelerate complex scientific workflows. In this work we introduce FoamPilot, a proof-of-concept LLM agent designed to enhance the usability of FireFOAM, a specialized solver for fire dynamics and fire supp... | {
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2412.17149 | A Multi-AI Agent System for Autonomous Optimization of Agentic AI
Solutions via Iterative Refinement and LLM-Driven Feedback Loops | [
"cs.CL",
"cs.AI",
"cs.ET",
"cs.MA",
"cs.NE"
] | Agentic AI systems use specialized agents to handle tasks within complex workflows, enabling automation and efficiency. However, optimizing these systems often requires labor-intensive, manual adjustments to refine roles, tasks, and interactions. This paper introduces a framework for autonomously optimizing Agentic AI ... | {
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2412.17150 | SplitFedZip: Learned Compression for Data Transfer Reduction in
Split-Federated Learning | [
"cs.LG"
] | Federated Learning (FL) enables multiple clients to train a collaborative model without sharing their local data. Split Learning (SL) allows a model to be trained in a split manner across different locations. Split-Federated (SplitFed) learning is a more recent approach that combines the strengths of FL and SL. SplitFe... | {
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2412.17152 | Unifying Feature-Based Explanations with Functional ANOVA and
Cooperative Game Theory | [
"cs.LG",
"stat.ML"
] | Feature-based explanations, using perturbations or gradients, are a prevalent tool to understand decisions of black box machine learning models. Yet, differences between these methods still remain mostly unknown, which limits their applicability for practitioners. In this work, we introduce a unified framework for loca... | {
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2412.17153 | Distilled Decoding 1: One-step Sampling of Image Auto-regressive Models
with Flow Matching | [
"cs.CV",
"cs.LG"
] | Autoregressive (AR) models have achieved state-of-the-art performance in text and image generation but suffer from slow generation due to the token-by-token process. We ask an ambitious question: can a pre-trained AR model be adapted to generate outputs in just one or two steps? If successful, this would significantly ... | {
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2412.17155 | The Potential of Convolutional Neural Networks for Cancer Detection | [
"cs.CV",
"cs.LG"
] | Early detection is a prime requisite for successful cancer treatment and increasing its survivability rates, particularly in the most common forms. CNNs (Convolutional Neural Networks) are very potent tools for the analysis and classification of medical images, with particular reference to the early detection of differ... | {
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2412.17156 | LLM-based relevance assessment still can't replace human relevance
assessment | [
"cs.IR"
] | The use of large language models (LLMs) for relevance assessment in information retrieval has gained significant attention, with recent studies suggesting that LLM-based judgments provide comparable evaluations to human judgments. Notably, based on TREC 2024 data, Upadhyay et al. make a bold claim that LLM-based releva... | {
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2412.17159 | Semantic Web: Past, Present, and Future | [
"cs.AI"
] | Ever since the vision was formulated, the Semantic Web has inspired many generations of innovations. Semantic technologies have been used to share vast amounts of information on the Web, enhance them with semantics to give them meaning, and enable inference and reasoning on them. Throughout the years, semantic technolo... | {
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2412.17160 | Unveiling the Potential of NOMA: A Journey to Next Generation Multiple
Access | [
"cs.IT",
"eess.SP",
"math.IT"
] | Revolutionary sixth-generation wireless communications technologies and applications, notably digital twin networks (DTN), connected autonomous vehicles (CAVs), space-air-ground integrated networks (SAGINs), zero-touch networks, industry 5.0, and healthcare 5.0, are driving next-generation wireless networks (NGWNs). Th... | {
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2412.17162 | Generative Diffusion Modeling: A Practical Handbook | [
"cs.LG",
"cs.CV"
] | This handbook offers a unified perspective on diffusion models, encompassing diffusion probabilistic models, score-based generative models, consistency models, rectified flow, and related methods. By standardizing notations and aligning them with code implementations, it aims to bridge the "paper-to-code" gap and facil... | {
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2412.17164 | Analysis of Speech Temporal Dynamics in the Context of Speaker
Verification and Voice Anonymization | [
"eess.AS",
"cs.CL",
"cs.SD"
] | In this paper, we investigate the impact of speech temporal dynamics in application to automatic speaker verification and speaker voice anonymization tasks. We propose several metrics to perform automatic speaker verification based only on phoneme durations. Experimental results demonstrate that phoneme durations leak ... | {
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2412.17165 | Survey on Abstractive Text Summarization: Dataset, Models, and Metrics | [
"cs.AI"
] | The advancements in deep learning, particularly the introduction of transformers, have been pivotal in enhancing various natural language processing (NLP) tasks. These include text-to-text applications such as machine translation, text classification, and text summarization, as well as data-to-text tasks like response ... | {
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2412.17170 | Where Did Your Model Learn That? Label-free Influence for
Self-supervised Learning | [
"cs.LG",
"cs.CV"
] | Self-supervised learning (SSL) has revolutionized learning from large-scale unlabeled datasets, yet the intrinsic relationship between pretraining data and the learned representations remains poorly understood. Traditional supervised learning benefits from gradient-based data attribution tools like influence functions ... | {
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2412.17171 | Enhancing Item Tokenization for Generative Recommendation through
Self-Improvement | [
"cs.LG",
"cs.IR"
] | Generative recommendation systems, driven by large language models (LLMs), present an innovative approach to predicting user preferences by modeling items as token sequences and generating recommendations in a generative manner. A critical challenge in this approach is the effective tokenization of items, ensuring that... | {
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2412.17175 | DCC: Differentiable Cardinality Constraints for Partial Index Tracking | [
"cs.AI",
"cs.CE"
] | Index tracking is a popular passive investment strategy aimed at optimizing portfolios, but fully replicating an index can lead to high transaction costs. To address this, partial replication have been proposed. However, the cardinality constraint renders the problem non-convex, non-differentiable, and often NP-hard, l... | {
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2412.17176 | WPMixer: Efficient Multi-Resolution Mixing for Long-Term Time Series
Forecasting | [
"cs.LG"
] | Time series forecasting is crucial for various applications, such as weather forecasting, power load forecasting, and financial analysis. In recent studies, MLP-mixer models for time series forecasting have been shown as a promising alternative to transformer-based models. However, the performance of these models is st... | {
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2412.17180 | COVID-19 on YouTube: A Data-Driven Analysis of Sentiment, Toxicity, and
Content Recommendations | [
"cs.SI",
"cs.AI",
"cs.CL",
"cs.CY",
"cs.LG"
] | This study presents a data-driven analysis of COVID-19 discourse on YouTube, examining the sentiment, toxicity, and thematic patterns of video content published between January 2023 and October 2024. The analysis involved applying advanced natural language processing (NLP) techniques: sentiment analysis with VADER, tox... | {
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2412.17183 | Thermodynamic computing out of equilibrium | [
"cond-mat.stat-mech",
"cs.LG",
"cs.NE"
] | We present the design for a thermodynamic computer that can perform arbitrary nonlinear calculations in or out of equilibrium. Simple thermodynamic circuits, fluctuating degrees of freedom in contact with a thermal bath and confined by a quartic potential, display an activity that is a nonlinear function of their input... | {
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2412.17184 | Foundation Model for Lossy Compression of Spatiotemporal Scientific Data | [
"cs.LG"
] | We present a foundation model (FM) for lossy scientific data compression, combining a variational autoencoder (VAE) with a hyper-prior structure and a super-resolution (SR) module. The VAE framework uses hyper-priors to model latent space dependencies, enhancing compression efficiency. The SR module refines low-resolut... | {
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2412.17188 | Hierarchically Gated Experts for Efficient Online Continual Learning | [
"cs.LG",
"cs.AI"
] | Continual Learning models aim to learn a set of tasks under the constraint that the tasks arrive sequentially with no way to access data from previous tasks. The Online Continual Learning framework poses a further challenge where the tasks are unknown and instead the data arrives as a single stream. Building on existin... | {
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2412.17189 | Better Think with Tables: Leveraging Tables to Enhance Large Language
Model Comprehension | [
"cs.AI"
] | Despite the recent advancement of Large Langauge Models (LLMs), they struggle with complex queries often involving multiple conditions, common in real-world scenarios. We propose Thinking with Tables, a technique that assists LLMs to leverage tables for intermediate thinking aligning with human cognitive behavior. By i... | {
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2412.17197 | Q-LIME $\pi$: A Quantum-Inspired Extension to LIME | [
"cs.LG",
"cs.AI"
] | Machine learning models offer powerful predictive capabilities but often lack transparency. Local Interpretable Model-agnostic Explanations (LIME) addresses this by perturbing features and measuring their impact on a model's output. In text-based tasks, LIME typically removes present words (bits set to 1) to identify h... | {
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2412.17210 | Dual Conditioned Motion Diffusion for Pose-Based Video Anomaly Detection | [
"cs.CV"
] | Video Anomaly Detection (VAD) is essential for computer vision research. Existing VAD methods utilize either reconstruction-based or prediction-based frameworks. The former excels at detecting irregular patterns or structures, whereas the latter is capable of spotting abnormal deviations or trends. We address pose-base... | {
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2412.17217 | Machine learning and natural language processing models to predict the
extent of food processing | [
"q-bio.BM",
"cs.LG"
] | The dramatic increase in consumption of ultra-processed food has been associated with numerous adverse health effects. Given the public health consequences linked to ultra-processed food consumption, it is highly relevant to build computational models to predict the processing of food products. We created a range of ma... | {
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2412.17219 | Discriminative Image Generation with Diffusion Models for Zero-Shot
Learning | [
"cs.CV"
] | Generative Zero-Shot Learning (ZSL) methods synthesize class-related features based on predefined class semantic prototypes, showcasing superior performance. However, this feature generation paradigm falls short of providing interpretable insights. In addition, existing approaches rely on semantic prototypes annotated ... | {
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2412.17225 | CharGen: High Accurate Character-Level Visual Text Generation Model with
MultiModal Encoder | [
"cs.CV"
] | Recently, significant advancements have been made in diffusion-based visual text generation models. Although the effectiveness of these methods in visual text rendering is rapidly improving, they still encounter challenges such as inaccurate characters and strokes when rendering complex visual text. In this paper, we p... | {
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2412.17226 | OLiDM: Object-aware LiDAR Diffusion Models for Autonomous Driving | [
"cs.CV",
"cs.RO"
] | To enhance autonomous driving safety in complex scenarios, various methods have been proposed to simulate LiDAR point cloud data. Nevertheless, these methods often face challenges in producing high-quality, diverse, and controllable foreground objects. To address the needs of object-aware tasks in 3D perception, we int... | {
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2412.17227 | Brain-to-Text Benchmark '24: Lessons Learned | [
"cs.CL",
"cs.LG",
"q-bio.NC"
] | Speech brain-computer interfaces aim to decipher what a person is trying to say from neural activity alone, restoring communication to people with paralysis who have lost the ability to speak intelligibly. The Brain-to-Text Benchmark '24 and associated competition was created to foster the advancement of decoding algor... | {
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2412.17228 | MatchMiner-AI: An Open-Source Solution for Cancer Clinical Trial
Matching | [
"cs.AI",
"cs.LG"
] | Clinical trials drive improvements in cancer treatments and outcomes. However, most adults with cancer do not participate in trials, and trials often fail to enroll enough patients to answer their scientific questions. Artificial intelligence could accelerate matching of patients to appropriate clinical trials. Here, w... | {
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2412.17231 | FedMeld: A Model-dispersal Federated Learning Framework for Space-ground
Integrated Networks | [
"cs.LG",
"cs.IT",
"cs.NI",
"math.IT"
] | To bridge the digital divide, the space-ground integrated networks (SGINs), which will be a key component of the six-generation (6G) mobile networks, are expected to deliver artificial intelligence (AI) services to every corner of the world. One mission of SGINs is to support federated learning (FL) at a global scale. ... | {
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2412.17234 | Accuracy and robust early detection of short-circuit faults in
single-cell lithium battery | [
"eess.SY",
"cs.SY"
] | Effective early-stage detection of internal short circuit in lithium-ion batteries is crucial to preventing thermal runaway. This report proposes an effective approach to address this challenging issue, in which the current change, state of charge and resistance are considered simultaneously to depict the voltage diffe... | {
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2412.17235 | Selective Kalman Filter: When and How to Fuse Multi-Sensor Information
to Overcome Degeneracy in SLAM | [
"cs.RO"
] | Research trends in SLAM systems are now focusing more on multi-sensor fusion to handle challenging and degenerative environments. However, most existing multi-sensor fusion SLAM methods mainly use all of the data from a range of sensors, a strategy we refer to as the all-in method. This method, while merging the benefi... | {
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2412.17238 | Modality-Aware Shot Relating and Comparing for Video Scene Detection | [
"cs.CV",
"cs.MM"
] | Video scene detection involves assessing whether each shot and its surroundings belong to the same scene. Achieving this requires meticulously correlating multi-modal cues, $\it{e.g.}$ visual entity and place modalities, among shots and comparing semantic changes around each shot. However, most methods treat multi-moda... | {
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2412.17239 | Unity is Strength: Unifying Convolutional and Transformeral Features for
Better Person Re-Identification | [
"cs.CV",
"cs.IR"
] | Person Re-identification (ReID) aims to retrieve the specific person across non-overlapping cameras, which greatly helps intelligent transportation systems. As we all know, Convolutional Neural Networks (CNNs) and Transformers have the unique strengths to extract local and global features, respectively. Considering thi... | {
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2412.17240 | Rethinking Cancer Gene Identification through Graph Anomaly Analysis | [
"cs.CE",
"cs.AI",
"stat.ML"
] | Graph neural networks (GNNs) have shown promise in integrating protein-protein interaction (PPI) networks for identifying cancer genes in recent studies. However, due to the insufficient modeling of the biological information in PPI networks, more faithfully depiction of complex protein interaction patterns for cancer ... | {
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2412.17241 | QTSeg: A Query Token-Based Dual-Mix Attention Framework with Multi-Level
Feature Distribution for Medical Image Segmentation | [
"cs.CV",
"cs.AI"
] | Medical image segmentation plays a crucial role in assisting healthcare professionals with accurate diagnoses and enabling automated diagnostic processes. Traditional convolutional neural networks (CNNs) often struggle with capturing long-range dependencies, while transformer-based architectures, despite their effectiv... | {
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2412.17242 | On the Generalization Ability of Machine-Generated Text Detectors | [
"cs.AI",
"cs.CL"
] | The rise of large language models (LLMs) has raised concerns about machine-generated text (MGT), including ethical and practical issues like plagiarism and misinformation. Building a robust and highly generalizable MGT detection system has become increasingly important. This work investigates the generalization capabil... | {
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2412.17243 | "From Unseen Needs to Classroom Solutions": Exploring AI Literacy
Challenges & Opportunities with Project-based Learning Toolkit in K-12
Education | [
"cs.AI",
"cs.CY",
"cs.HC"
] | As artificial intelligence (AI) becomes increasingly central to various fields, there is a growing need to equip K-12 students with AI literacy skills that extend beyond computer science. This paper explores the integration of a Project-Based Learning (PBL) AI toolkit into diverse subject areas, aimed at helping educat... | {
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2412.17245 | GraphHash: Graph Clustering Enables Parameter Efficiency in Recommender
Systems | [
"cs.IR",
"cs.SI"
] | Deep recommender systems rely heavily on large embedding tables to handle high-cardinality categorical features such as user/item identifiers, and face significant memory constraints at scale. To tackle this challenge, hashing techniques are often employed to map multiple entities to the same embedding and thus reduce ... | {
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} |
2412.17247 | STeInFormer: Spatial-Temporal Interaction Transformer Architecture for
Remote Sensing Change Detection | [
"cs.CV"
] | Convolutional neural networks and attention mechanisms have greatly benefited remote sensing change detection (RSCD) because of their outstanding discriminative ability. Existent RSCD methods often follow a paradigm of using a non-interactive Siamese neural network for multi-temporal feature extraction and change detec... | {
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2412.17249 | EM-MIAs: Enhancing Membership Inference Attacks in Large Language Models
through Ensemble Modeling | [
"cs.RO",
"cs.CR"
] | With the widespread application of large language models (LLM), concerns about the privacy leakage of model training data have increasingly become a focus. Membership Inference Attacks (MIAs) have emerged as a critical tool for evaluating the privacy risks associated with these models. Although existing attack methods,... | {
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2412.17250 | SyNeg: LLM-Driven Synthetic Hard-Negatives for Dense Retrieval | [
"cs.IR"
] | The performance of Dense retrieval (DR) is significantly influenced by the quality of negative sampling. Traditional DR methods primarily depend on naive negative sampling techniques or on mining hard negatives through external retriever and meticulously crafted strategies. However, naive negative sampling often fails ... | {
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2412.17251 | GCS-M3VLT: Guided Context Self-Attention based Multi-modal Medical
Vision Language Transformer for Retinal Image Captioning | [
"cs.CV",
"cs.LG",
"eess.IV"
] | Retinal image analysis is crucial for diagnosing and treating eye diseases, yet generating accurate medical reports from images remains challenging due to variability in image quality and pathology, especially with limited labeled data. Previous Transformer-based models struggled to integrate visual and textual informa... | {
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} |
2412.17252 | A Coalition Game for On-demand Multi-modal 3D Automated Delivery System | [
"cs.LG",
"math.OC"
] | We introduce a multi-modal autonomous delivery optimization framework as a coalition game for a fleet of UAVs and ADRs operating in two overlaying networks to address last-mile delivery in urban environments, including high-density areas, road-based routing, and real-world operational challenges. The problem is defined... | {
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} |
2412.17254 | Enhancing Multi-Text Long Video Generation Consistency without Tuning:
Time-Frequency Analysis, Prompt Alignment, and Theory | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Despite the considerable progress achieved in the long video generation problem, there is still significant room to improve the consistency of the videos, particularly in terms of smoothness and transitions between scenes. We address these issues to enhance the consistency and coherence of videos generated with either ... | {
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} |
2412.17255 | Unlocking Cross-Lingual Sentiment Analysis through Emoji Interpretation:
A Multimodal Generative AI Approach | [
"cs.CL",
"cs.AI"
] | Emojis have become ubiquitous in online communication, serving as a universal medium to convey emotions and decorative elements. Their widespread use transcends language and cultural barriers, enhancing understanding and fostering more inclusive interactions. While existing work gained valuable insight into emojis unde... | {
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"cs.SY": 0
} |
2412.17256 | B-STaR: Monitoring and Balancing Exploration and Exploitation in
Self-Taught Reasoners | [
"cs.AI",
"cs.CL",
"cs.LG"
] | In the absence of extensive human-annotated data for complex reasoning tasks, self-improvement -- where models are trained on their own outputs -- has emerged as a primary method for enhancing performance. However, the critical factors underlying the mechanism of these iterative self-improving methods remain poorly und... | {
"Other": 0,
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} |
2412.17258 | An Intrinsically Explainable Approach to Detecting Vertebral Compression
Fractures in CT Scans via Neurosymbolic Modeling | [
"cs.LG",
"cs.CV",
"eess.IV"
] | Vertebral compression fractures (VCFs) are a common and potentially serious consequence of osteoporosis. Yet, they often remain undiagnosed. Opportunistic screening, which involves automated analysis of medical imaging data acquired primarily for other purposes, is a cost-effective method to identify undiagnosed VCFs. ... | {
"Other": 0,
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"cs.NE": 0,
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"cs.SD": 0,
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"cs.SY": 0
} |
2412.17259 | LegalAgentBench: Evaluating LLM Agents in Legal Domain | [
"cs.CL",
"cs.IR"
] | With the increasing intelligence and autonomy of LLM agents, their potential applications in the legal domain are becoming increasingly apparent. However, existing general-domain benchmarks cannot fully capture the complexity and subtle nuances of real-world judicial cognition and decision-making. Therefore, we propose... | {
"Other": 0,
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"cs.SD": 0,
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} |
2412.17263 | VarAD: Lightweight High-Resolution Image Anomaly Detection via Visual
Autoregressive Modeling | [
"cs.CV"
] | This paper addresses a practical task: High-Resolution Image Anomaly Detection (HRIAD). In comparison to conventional image anomaly detection for low-resolution images, HRIAD imposes a heavier computational burden and necessitates superior global information capture capacity. To tackle HRIAD, this paper translates imag... | {
"Other": 0,
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} |
2412.17265 | Evaluating the Design Features of an Intelligent Tutoring System for
Advanced Mathematics Learning | [
"cs.MS",
"cs.AI",
"cs.CY",
"math.HO"
] | Xiaomai is an intelligent tutoring system (ITS) designed to help Chinese college students in learning advanced mathematics and preparing for the graduate school math entrance exam. This study investigates two distinctive features within Xiaomai: the incorporation of free-response questions with automatic feedback and t... | {
"Other": 1,
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} |
2412.17268 | STAR-RIS Assisted SWIPT Systems: Active or Passive? | [
"cs.IT",
"eess.SP",
"math.IT"
] | A simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted simultaneous wireless information and power transfer (SWIPT) system is investigated. Both active and passive STAR-RISs are considered. Passive STAR-RISs can be cost-efficiently fabricated to large aperture sizes with sig... | {
"Other": 0,
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"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
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