id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2501.15087 | PatchRec: Multi-Grained Patching for Efficient LLM-based Sequential
Recommendation | [
"cs.IR"
] | Large Language Models for sequential recommendation (LLM4SR), which transform user-item interactions into language modeling, have shown promising results. However, due to the limitations of context window size and the computational costs associated with Large Language Models (LLMs), current approaches primarily truncat... | {
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2501.15089 | LongReason: A Synthetic Long-Context Reasoning Benchmark via Context
Expansion | [
"cs.CL"
] | Large language models (LLMs) have demonstrated remarkable progress in understanding long-context inputs. However, benchmarks for evaluating the long-context reasoning abilities of LLMs fall behind the pace. Existing benchmarks often focus on a narrow range of tasks or those that do not demand complex reasoning. To addr... | {
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2501.15090 | Speech Translation Refinement using Large Language Models | [
"cs.CL"
] | Recent advancements in large language models (LLMs) have demonstrated their remarkable capabilities across various language tasks. Inspired by the success of text-to-text translation refinement, this paper investigates how LLMs can improve the performance of speech translation by introducing a joint refinement process.... | {
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2501.15091 | Deep Reinforcement Learning for Energy Efficiency Maximization in
RSMA-IRS-Assisted ISAC System | [
"cs.IT",
"eess.SP",
"math.IT"
] | This paper proposes a three-dimensional (3D) geometry-based channel model to accurately represent intelligent reflecting surfaces (IRS)-enhanced integrated sensing and communication (ISAC) networks using rate-splitting multiple access (RSMA) in practical urban environments. Based on this model, we formulate an energy e... | {
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2501.15096 | Towards Better Robustness: Progressively Joint Pose-3DGS Learning for
Arbitrarily Long Videos | [
"cs.CV"
] | 3D Gaussian Splatting (3DGS) has emerged as a powerful representation due to its efficiency and high-fidelity rendering. However, 3DGS training requires a known camera pose for each input view, typically obtained by Structure-from-Motion (SfM) pipelines. Pioneering works have attempted to relax this restriction but sti... | {
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2501.15098 | CFT-RAG: An Entity Tree Based Retrieval Augmented Generation Algorithm
With Cuckoo Filter | [
"cs.LG",
"cs.AI"
] | Although retrieval-augmented generation(RAG) significantly improves generation quality by retrieving external knowledge bases and integrating generated content, it faces computational efficiency bottlenecks, particularly in knowledge retrieval tasks involving hierarchical structures for Tree-RAG. This paper proposes a ... | {
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2501.15099 | Bringing RGB and IR Together: Hierarchical Multi-Modal Enhancement for
Robust Transmission Line Detection | [
"cs.CV",
"cs.LG"
] | Ensuring a stable power supply in rural areas relies heavily on effective inspection of power equipment, particularly transmission lines (TLs). However, detecting TLs from aerial imagery can be challenging when dealing with misalignments between visible light (RGB) and infrared (IR) images, as well as mismatched high- ... | {
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2501.15103 | Each Rank Could be an Expert: Single-Ranked Mixture of Experts LoRA for
Multi-Task Learning | [
"cs.LG",
"cs.AI"
] | Low-Rank Adaptation (LoRA) is widely used for adapting large language models (LLMs) to specific domains due to its efficiency and modularity. Meanwhile, vanilla LoRA struggles with task conflicts in multi-task scenarios. Recent works adopt Mixture of Experts (MoE) by treating each LoRA module as an expert, thereby miti... | {
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2501.15105 | A New Approach for Knowledge Generation Using Active Inference | [
"cs.AI",
"q-bio.NC"
] | There are various models proposed on how knowledge is generated in the human brain including the semantic networks model. Although this model has been widely studied and even computational models are presented, but, due to various limits and inefficiencies in the generation of different types of knowledge, its applicat... | {
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2501.15106 | In-Context Operator Learning for Linear Propagator Models | [
"q-fin.TR",
"cs.LG",
"math.OC",
"q-fin.CP"
] | We study operator learning in the context of linear propagator models for optimal order execution problems with transient price impact \`a la Bouchaud et al. (2004) and Gatheral (2010). Transient price impact persists and decays over time according to some propagator kernel. Specifically, we propose to use In-Context O... | {
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2501.15108 | Knowledge Hierarchy Guided Biological-Medical Dataset Distillation for
Domain LLM Training | [
"cs.CL"
] | The rapid advancement of large language models (LLMs) in biological-medical applications has highlighted a gap between their potential and the limited scale and often low quality of available open-source annotated textual datasets. In addition, the inherent complexity of the biomedical knowledge hierarchy significantly... | {
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2501.15109 | Clear Preferences Leave Traces: Reference Model-Guided Sampling for
Preference Learning | [
"cs.LG",
"cs.AI"
] | Direct Preference Optimization (DPO) has emerged as a de-facto approach for aligning language models with human preferences. Recent work has shown DPO's effectiveness relies on training data quality. In particular, clear quality differences between preferred and rejected responses enhance learning performance. Current ... | {
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2501.15111 | HumanOmni: A Large Vision-Speech Language Model for Human-Centric Video
Understanding | [
"cs.CV"
] | In human-centric scenes, the ability to simultaneously understand visual and auditory information is crucial. While recent omni models can process multiple modalities, they generally lack effectiveness in human-centric scenes due to the absence of large-scale, specialized datasets and non-targeted architectures. In thi... | {
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2501.15113 | Task-KV: Task-aware KV Cache Optimization via Semantic Differentiation
of Attention Heads | [
"cs.CL"
] | KV cache is a widely used acceleration technique for large language models (LLMs) inference. However, its memory requirement grows rapidly with input length. Previous studies have reduced the size of KV cache by either removing the same number of unimportant tokens for all attention heads or by allocating differentiate... | {
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2501.15118 | ABXI: Invariant Interest Adaptation for Task-Guided Cross-Domain
Sequential Recommendation | [
"cs.IR"
] | Cross-Domain Sequential Recommendation (CDSR) has recently gained attention for countering data sparsity by transferring knowledge across domains. A common approach merges domain-specific sequences into cross-domain sequences, serving as bridges to connect domains. One key challenge is to correctly extract the shared k... | {
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2501.15119 | Efficient Video Neural Network Processing Based on Motion Estimation | [
"cs.CV",
"eess.IV"
] | Video neural network (VNN) processing using the conventional pipeline first converts Bayer video information into human understandable RGB videos using image signal processing (ISP) on a pixel by pixel basis. Then, VNN processing is performed on a frame by frame basis. Both ISP and VNN are computationally expensive wit... | {
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2501.15120 | Technology Mapping with Large Language Models | [
"cs.IR",
"cs.DB",
"cs.ET",
"cs.LG"
] | In today's fast-evolving business landscape, having insight into the technology stacks that organizations use is crucial for forging partnerships, uncovering market openings, and informing strategic choices. However, conventional technology mapping, which typically hinges on keyword searches, struggles with the sheer s... | {
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2501.15122 | Snapshot Compressed Imaging Based Single-Measurement Computer Vision for
Videos | [
"cs.CV",
"cs.AI"
] | Snapshot compressive imaging (SCI) is a promising technique for capturing high-speed video at low bandwidth and low power, typically by compressing multiple frames into a single measurement. However, similar to traditional CMOS image sensor based imaging systems, SCI also faces challenges in low-lighting photon-limited... | {
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2501.15125 | FreqMoE: Enhancing Time Series Forecasting through Frequency
Decomposition Mixture of Experts | [
"cs.LG"
] | Long-term time series forecasting is essential in areas like finance and weather prediction. Besides traditional methods that operate in the time domain, many recent models transform time series data into the frequency domain to better capture complex patterns. However, these methods often use filtering techniques to r... | {
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2501.15128 | MAP-based Problem-Agnostic diffusion model for Inverse Problems | [
"eess.IV",
"cs.CV"
] | Diffusion models have indeed shown great promise in solving inverse problems in image processing. In this paper, we propose a novel, problem-agnostic diffusion model called the maximum a posteriori (MAP)-based guided term estimation method for inverse problems. We divide the conditional score function into two terms ac... | {
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2501.15129 | EvoRL: A GPU-accelerated Framework for Evolutionary Reinforcement
Learning | [
"cs.NE"
] | Evolutionary Reinforcement Learning (EvoRL) has emerged as a promising approach to overcoming the limitations of traditional reinforcement learning (RL) by integrating the Evolutionary Computation (EC) paradigm with RL. However, the population-based nature of EC significantly increases computational costs, thereby rest... | {
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2501.15130 | Community Detection in Large-Scale Complex Networks via Structural
Entropy Game | [
"cs.SI"
] | Community detection is a critical task in graph theory, social network analysis, and bioinformatics, where communities are defined as clusters of densely interconnected nodes. However, detecting communities in large-scale networks with millions of nodes and billions of edges remains challenging due to the inefficiency ... | {
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2501.15131 | Difference vs. Quotient: A Novel Algorithm for Dominant Eigenvalue
Problem | [
"math.OC",
"cs.LG"
] | The computation of the dominant eigenvector of symmetric positive semidefinite matrices is a cornerstone operation in numerous machine learning applications. Traditional approaches predominantly rely on the constrained Quotient formulation, which underpins most existing methods. However, these methods often suffer from... | {
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2501.15138 | TranStable: Towards Robust Pixel-level Online Video Stabilization by
Jointing Transformer and CNN | [
"cs.CV"
] | Video stabilization often struggles with distortion and excessive cropping. This paper proposes a novel end-to-end framework, named TranStable, to address these challenges, comprising a genera tor and a discriminator. We establish TransformerUNet (TUNet) as the generator to utilize the Hierarchical Adaptive Fusion Modu... | {
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2501.15140 | Analyzing and Boosting the Power of Fine-Grained Visual Recognition for
Multi-modal Large Language Models | [
"cs.CV",
"cs.AI",
"cs.CL",
"cs.LG"
] | Multi-modal large language models (MLLMs) have shown remarkable abilities in various visual understanding tasks. However, MLLMs still struggle with fine-grained visual recognition (FGVR), which aims to identify subordinate-level categories from images. This can negatively impact more advanced capabilities of MLLMs, suc... | {
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2501.15142 | DAGPrompT: Pushing the Limits of Graph Prompting with a
Distribution-aware Graph Prompt Tuning Approach | [
"cs.LG",
"cs.AI"
] | The pre-train then fine-tune approach has advanced GNNs by enabling general knowledge capture without task-specific labels. However, an objective gap between pre-training and downstream tasks limits its effectiveness. Recent graph prompting methods aim to close this gap through task reformulations and learnable prompts... | {
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2501.15144 | Exploring Primitive Visual Measurement Understanding and the Role of
Output Format in Learning in Vision-Language Models | [
"cs.CV"
] | This work investigates the capabilities of current vision-language models (VLMs) in visual understanding and attribute measurement of primitive shapes using a benchmark focused on controlled 2D shape configurations with variations in spatial positioning, occlusion, rotation, size, and shape attributes such as type, qua... | {
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2501.15147 | A Causality-aware Paradigm for Evaluating Creativity of Multimodal Large
Language Models | [
"cs.AI",
"cs.HC"
] | Recently, numerous benchmarks have been developed to evaluate the logical reasoning abilities of large language models (LLMs). However, assessing the equally important creative capabilities of LLMs is challenging due to the subjective, diverse, and data-scarce nature of creativity, especially in multimodal scenarios. I... | {
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2501.15149 | Mapping Galaxy Images Across Ultraviolet, Visible and Infrared Bands
Using Generative Deep Learning | [
"astro-ph.IM",
"astro-ph.GA",
"cs.AI"
] | We demonstrate that generative deep learning can translate galaxy observations across ultraviolet, visible, and infrared photometric bands. Leveraging mock observations from the Illustris simulations, we develop and validate a supervised image-to-image model capable of performing both band interpolation and extrapolati... | {
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2501.15151 | SpikSSD: Better Extraction and Fusion for Object Detection with Spiking
Neuron Networks | [
"cs.CV"
] | As the third generation of neural networks, Spiking Neural Networks (SNNs) have gained widespread attention due to their low energy consumption and biological interpretability. Recently, SNNs have made considerable advancements in computer vision. However, efficiently conducting feature extraction and fusion under the ... | {
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2501.15157 | Median of Forests for Robust Density Estimation | [
"stat.ML",
"cs.LG"
] | Robust density estimation refers to the consistent estimation of the density function even when the data is contaminated by outliers. We find that existing forest density estimation at a certain point is inherently resistant to the outliers outside the cells containing the point, which we call \textit{non-local outlier... | {
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2501.15163 | Learning with Noisy Labels: the Exploration of Error Bounds in
Classification | [
"cs.LG",
"stat.ML"
] | Numerous studies have shown that label noise can lead to poor generalization performance, negatively affecting classification accuracy. Therefore, understanding the effectiveness of classifiers trained using deep neural networks in the presence of noisy labels is of considerable practical significance. In this paper, w... | {
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2501.15164 | UAV-Assisted MEC Architecture for Collaborative Task Offloading in Urban
IoT Environment | [
"cs.NI",
"cs.SY",
"eess.SP",
"eess.SY"
] | Mobile edge computing (MEC) is a promising technology to meet the increasing demands and computing limitations of complex Internet of Things (IoT) devices. However, implementing MEC in urban environments can be challenging due to factors like high device density, complex infrastructure, and limited network coverage. Ne... | {
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2501.15165 | A* Based Algorithm for Reduced Complexity ML Decoding of Tailbiting
Codes | [
"cs.IT",
"math.IT"
] | The A* algorithm is a graph search algorithm which has shown good results in terms of computational complexity for Maximum Likelihood (ML) decoding of tailbiting convolutional codes. The decoding of tailbiting codes with this algorithm is performed in two phases. In the first phase, a typical Viterbi decoding is employ... | {
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2501.15167 | Enhancing Intent Understanding for Ambiguous Prompts through
Human-Machine Co-Adaptation | [
"cs.CV"
] | Today's image generation systems are capable of producing realistic and high-quality images. However, user prompts often contain ambiguities, making it difficult for these systems to interpret users' actual intentions. Consequently, many users must modify their prompts several times to ensure the generated images meet ... | {
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2501.15172 | DeepDIVE: Optimizing Input-Constrained Distributions for Composite DNA
Storage via Multinomial Channel | [
"cs.IT",
"eess.SP",
"math.IT"
] | We address the challenge of optimizing the capacity-achieving input distribution for a multinomial channel under the constraint of limited input support size, which is a crucial aspect in the design of DNA storage systems. We propose an algorithm that further elaborates the Multidimensional Dynamic Assignment Blahut-Ar... | {
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2501.15174 | On Spectral Approach to the Synthesis of Shaping Filters | [
"eess.SY",
"cs.SY",
"math.OC",
"math.PR"
] | This paper describes various approaches to modeling a random process with a given rational power spectral density. The main attention is paid to the spectral form of mathematical description, which allows one to obtain a relation for the shaping filter using a transfer function without any additional calculations. The ... | {
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2501.15175 | Option-ID Based Elimination For Multiple Choice Questions | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Multiple choice questions (MCQs) are a popular and important task for evaluating large language models (LLMs). Based on common strategies people use when answering MCQs, the process of elimination (PoE) has been proposed as an effective problem-solving method. Existing methods to the PoE generally fall into two categor... | {
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2501.15183 | Generating Negative Samples for Multi-Modal Recommendation | [
"cs.IR"
] | Multi-modal recommender systems (MMRS) have gained significant attention due to their ability to leverage information from various modalities to enhance recommendation quality. However, existing negative sampling techniques often struggle to effectively utilize the multi-modal data, leading to suboptimal performance. I... | {
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2501.15186 | An Iterative Deep Ritz Method for Monotone Elliptic Problems | [
"math.NA",
"cs.LG",
"cs.NA"
] | In this work, we present a novel iterative deep Ritz method (IDRM) for solving a general class of elliptic problems. It is inspired by the iterative procedure for minimizing the loss during the training of the neural network, but at each step encodes the geometry of the underlying function space and incorporates a conv... | {
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2501.15187 | Uni-Sign: Toward Unified Sign Language Understanding at Scale | [
"cs.CV"
] | Sign language pre-training has gained increasing attention for its ability to enhance performance across various sign language understanding (SLU) tasks. However, existing methods often suffer from a gap between pre-training and fine-tuning, leading to suboptimal results. To address this, we propose Uni-Sign, a unified... | {
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2501.15188 | Who is the root in a syntactic dependency structure? | [
"cs.CL",
"cs.SI",
"physics.soc-ph"
] | The syntactic structure of a sentence can be described as a tree that indicates the syntactic relationships between words. In spite of significant progress in unsupervised methods that retrieve the syntactic structure of sentences, guessing the right direction of edges is still a challenge. As in a syntactic dependency... | {
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2501.15189 | Extracting Forward Invariant Sets from Neural Network-Based Control
Barrier Functions | [
"cs.LG",
"cs.RO",
"cs.SY",
"eess.SY",
"stat.ML"
] | Training Neural Networks (NNs) to serve as Barrier Functions (BFs) is a popular way to improve the safety of autonomous dynamical systems. Despite significant practical success, these methods are not generally guaranteed to produce true BFs in a provable sense, which undermines their intended use as safety certificates... | {
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2501.15190 | A Floating Normalization Scheme for Deep Learning-Based Custom-Range
Parameter Extraction in BSIM-CMG Compact Models | [
"cs.LG",
"eess.SP"
] | A deep-learning (DL) based methodology for automated extraction of BSIM-CMG compact model parameters from experimental gate capacitance vs gate voltage (Cgg-Vg) and drain current vs gate voltage (Id-Vg) measurements is proposed in this paper. The proposed method introduces a floating normalization scheme within a casca... | {
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2501.15194 | Reliable Pseudo-labeling via Optimal Transport with Attention for Short
Text Clustering | [
"cs.LG",
"stat.CO",
"stat.ML"
] | Short text clustering has gained significant attention in the data mining community. However, the limited valuable information contained in short texts often leads to low-discriminative representations, increasing the difficulty of clustering. This paper proposes a novel short text clustering framework, called Reliable... | {
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2501.15196 | A Review on Self-Supervised Learning for Time Series Anomaly Detection:
Recent Advances and Open Challenges | [
"stat.ML",
"cs.LG"
] | Time series anomaly detection presents various challenges due to the sequential and dynamic nature of time-dependent data. Traditional unsupervised methods frequently encounter difficulties in generalization, often overfitting to known normal patterns observed during training and struggling to adapt to unseen normality... | {
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2501.15198 | Towards Conscious Service Robots | [
"cs.RO",
"cs.AI"
] | Deep learning's success in perception, natural language processing, etc. inspires hopes for advancements in autonomous robotics. However, real-world robotics face challenges like variability, high-dimensional state spaces, non-linear dependencies, and partial observability. A key issue is non-stationarity of robots, en... | {
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2501.15201 | A Training-free Synthetic Data Selection Method for Semantic
Segmentation | [
"cs.CV"
] | Training semantic segmenter with synthetic data has been attracting great attention due to its easy accessibility and huge quantities. Most previous methods focused on producing large-scale synthetic image-annotation samples and then training the segmenter with all of them. However, such a solution remains a main chall... | {
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2501.15203 | Reinforcement Learning Controlled Adaptive PSO for Task Offloading in
IIoT Edge Computing | [
"cs.LG",
"cs.DC"
] | Industrial Internet of Things (IIoT) applications demand efficient task offloading to handle heavy data loads with minimal latency. Mobile Edge Computing (MEC) brings computation closer to devices to reduce latency and server load, optimal performance requires advanced optimization techniques. We propose a novel soluti... | {
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2501.15206 | Engineering-Oriented Design of Drift-Resilient MTJ Random Number
Generator via Hybrid Control Strategies | [
"physics.app-ph",
"cond-mat.dis-nn",
"cs.SY",
"eess.SY"
] | In the quest for secure and reliable random number generation, Magnetic Tunnel Junctions (MTJs) have emerged as a promising technology due to their unique ability to exploit the stochastic nature of magnetization switching. This paper presents an engineering-oriented design of a drift-resilient MTJ-based True Random Nu... | {
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2501.15207 | Hybrid Near/Far-Field Frequency-Dependent Beamforming via Joint
Phase-Time Arrays | [
"cs.IT",
"eess.SP",
"math.IT"
] | Joint phase-time arrays (JPTA) emerge as a cost-effective and energy-efficient architecture for frequency-dependent beamforming in wideband communications by utilizing both true-time delay units and phase shifters. This paper exploits the potential of JPTA to simultaneously serve multiple users in both near- and far-fi... | {
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2501.15211 | "Stones from Other Hills can Polish Jade": Zero-shot Anomaly Image
Synthesis via Cross-domain Anomaly Injection | [
"cs.CV"
] | Industrial image anomaly detection (IAD) is a pivotal topic with huge value. Due to anomaly's nature, real anomalies in a specific modern industrial domain (i.e. domain-specific anomalies) are usually too rare to collect, which severely hinders IAD. Thus, zero-shot anomaly synthesis (ZSAS), which synthesizes pseudo ano... | {
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2501.15214 | Zero-shot Robotic Manipulation with Language-guided Instruction and
Formal Task Planning | [
"cs.RO",
"cs.LG"
] | Robotic manipulation is often challenging due to the long-horizon tasks and the complex object relationships. A common solution is to develop a task and motion planning framework that integrates planning for high-level task and low-level motion. Recently, inspired by the powerful reasoning ability of Large Language Mod... | {
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2501.15217 | Predictive Lagrangian Optimization for Constrained Reinforcement
Learning | [
"cs.LG",
"cs.SY",
"eess.SY"
] | Constrained optimization is popularly seen in reinforcement learning for addressing complex control tasks. From the perspective of dynamic system, iteratively solving a constrained optimization problem can be framed as the temporal evolution of a feedback control system. Classical constrained optimization methods, such... | {
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2501.15219 | Faster Machine Translation Ensembling with Reinforcement Learning and
Competitive Correction | [
"cs.CL"
] | Ensembling neural machine translation (NMT) models to produce higher-quality translations than the $L$ individual models has been extensively studied. Recent methods typically employ a candidate selection block (CSB) and an encoder-decoder fusion block (FB), requiring inference across \textit{all} candidate models, lea... | {
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2501.15221 | Performance analysis of tail-minimization and the linear rate of
convergence of a proximal algorithm for sparse signal recovery | [
"cs.IT",
"math.IT"
] | Recovery error bounds of tail-minimization and the rate of convergence of an efficient proximal alternating algorithm for sparse signal recovery are considered in this article. Tail-minimization focuses on minimizing the energy in the complement $T^c$ of an estimated support $T$. Under the restricted isometry property ... | {
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2501.15223 | Efficient and Interpretable Neural Networks Using Complex Lehmer
Transform | [
"cs.LG",
"cs.AI"
] | We propose an efficient and interpretable neural network with a novel activation function called the weighted Lehmer transform. This new activation function enables adaptive feature selection and extends to the complex domain, capturing phase-sensitive and hierarchical relationships within data. Notably, it provides gr... | {
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2501.15225 | SEAL: Scaling to Emphasize Attention for Long-Context Retrieval | [
"cs.CL",
"cs.AI",
"cs.LG"
] | In this work, we introduce a novel approach called Scaling to Emphasize Attention for Long-context retrieval (SEAL), which enhances the retrieval performance of large language models (LLMs) over extended contexts. Previous studies have shown that each attention head in LLMs has a unique functionality and collectively c... | {
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2501.15227 | Detecting Unauthorized Drones with Cell-Free Integrated Sensing and
Communication | [
"cs.IT",
"eess.SP",
"math.IT"
] | Integrated sensing and communication (ISAC) boosts network efficiency by using existing resources for diverse sensing applications. In this work, we propose a cell-free massive MIMO (multiple-input multiple-output)-ISAC framework to detect unauthorized drones while simultaneously ensuring communication requirements. We... | {
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2501.15228 | Improving Retrieval-Augmented Generation through Multi-Agent
Reinforcement Learning | [
"cs.CL",
"cs.IR"
] | Retrieval-augmented generation (RAG) is extensively utilized to incorporate external, current knowledge into large language models, thereby minimizing hallucinations. A standard RAG pipeline may comprise several components, such as query rewriting, document retrieval, document filtering, and answer generation. However,... | {
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2501.15235 | Large-Scale Riemannian Meta-Optimization via Subspace Adaptation | [
"cs.LG",
"cs.CV"
] | Riemannian meta-optimization provides a promising approach to solving non-linear constrained optimization problems, which trains neural networks as optimizers to perform optimization on Riemannian manifolds. However, existing Riemannian meta-optimization methods take up huge memory footprints in large-scale optimizatio... | {
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2501.15240 | Hardware-Aware DNN Compression for Homogeneous Edge Devices | [
"cs.LG",
"cs.AI"
] | Deploying deep neural networks (DNNs) across homogeneous edge devices (the devices with the same SKU labeled by the manufacturer) often assumes identical performance among them. However, once a device model is widely deployed, the performance of each device becomes different after a period of running. This is caused by... | {
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2501.15245 | ASRank: Zero-Shot Re-Ranking with Answer Scent for Document Retrieval | [
"cs.CL"
] | Retrieval-Augmented Generation (RAG) models have drawn considerable attention in modern open-domain question answering. The effectiveness of RAG depends on the quality of the top retrieved documents. However, conventional retrieval methods sometimes fail to rank the most relevant documents at the top. In this paper, we... | {
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2501.15247 | Prompting ChatGPT for Chinese Learning as L2: A CEFR and EBCL Level
Study | [
"cs.CL",
"cs.AI"
] | The use of chatbots in language learning has evolved significantly since the 1960s, becoming more sophisticated platforms as generative AI emerged. These tools now simulate natural conversations, adapting to individual learners' needs, including those studying Chinese. Our study explores how learners can use specific p... | {
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2501.15248 | Enhancing Fetal Plane Classification Accuracy with Data Augmentation
Using Diffusion Models | [
"cs.CV"
] | Ultrasound imaging is widely used in medical diagnosis, especially for fetal health assessment. However, the availability of high-quality annotated ultrasound images is limited, which restricts the training of machine learning models. In this paper, we investigate the use of diffusion models to generate synthetic ultra... | {
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2501.15249 | An Automatic Sound and Complete Abstraction Method for Generalized
Planning with Baggable Types | [
"cs.AI"
] | Generalized planning is concerned with how to find a single plan to solve multiple similar planning instances. Abstractions are widely used for solving generalized planning, and QNP (qualitative numeric planning) is a popular abstract model. Recently, Cui et al. showed that a plan solves a sound and complete abstractio... | {
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2501.15253 | Generalizable Deepfake Detection via Effective Local-Global Feature
Extraction | [
"cs.CV"
] | The rapid advancement of GANs and diffusion models has led to the generation of increasingly realistic fake images, posing significant hidden dangers and threats to society. Consequently, deepfake detection has become a pressing issue in today's world. While some existing methods focus on forgery features from either a... | {
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2501.15255 | Lightweight and Post-Training Structured Pruning for On-Device Large
Lanaguage Models | [
"cs.LG",
"cs.AI"
] | Considering the hardware-friendly characteristics and broad applicability, structured pruning has emerged as an efficient solution to reduce the resource demands of large language models (LLMs) on resource-constrained devices. Traditional structured pruning methods often need fine-tuning to recover performance loss, wh... | {
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2501.15257 | Pre-trained Model Guided Mixture Knowledge Distillation for Adversarial
Federated Learning | [
"cs.CV"
] | This paper aims to improve the robustness of a small global model while maintaining clean accuracy under adversarial attacks and non-IID challenges in federated learning. By leveraging the concise knowledge embedded in the class probabilities from a pre-trained model for both clean and adversarial image classification,... | {
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2501.15259 | Scalable Decentralized Learning with Teleportation | [
"cs.LG",
"math.OC",
"stat.ML"
] | Decentralized SGD can run with low communication costs, but its sparse communication characteristics deteriorate the convergence rate, especially when the number of nodes is large. In decentralized learning settings, communication is assumed to occur on only a given topology, while in many practical cases, the topology... | {
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2501.15260 | Breaking the Stigma! Unobtrusively Probe Symptoms in Depression Disorder
Diagnosis Dialogue | [
"cs.CL",
"cs.CY"
] | Stigma has emerged as one of the major obstacles to effectively diagnosing depression, as it prevents users from open conversations about their struggles. This requires advanced questioning skills to carefully probe the presence of specific symptoms in an unobtrusive manner. While recent efforts have been made on depre... | {
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2501.15262 | Dynamic Estimation of Tea Flowering Based on an Improved YOLOv5 and ANN
Model | [
"cs.CV",
"q-bio.QM"
] | Tea flowers play a crucial role in taxonomic research and hybrid breeding for the tea plant. Tea flowering consumes the plant's nutrients, and flower thinning can regulate carbon-nitrogen metabolism, enhancing the yield and quality of young shoots. As traditional methods of observing tea flower traits are labor-intensi... | {
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2501.15263 | Explainable YOLO-Based Dyslexia Detection in Synthetic Handwriting Data | [
"cs.CV",
"cs.LG"
] | Dyslexia affects reading and writing skills across many languages. This work describes a new application of YOLO-based object detection to isolate and label handwriting patterns (Normal, Reversal, Corrected) within synthetic images that resemble real words. Individual letters are first collected, preprocessed into 32x3... | {
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2501.15265 | Kernel-Based Anomaly Detection Using Generalized Hyperbolic Processes | [
"cs.LG"
] | We present a novel approach to anomaly detection by integrating Generalized Hyperbolic (GH) processes into kernel-based methods. The GH distribution, known for its flexibility in modeling skewness, heavy tails, and kurtosis, helps to capture complex patterns in data that deviate from Gaussian assumptions. We propose a ... | {
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2501.15266 | Enhanced Intrusion Detection in IIoT Networks: A Lightweight Approach
with Autoencoder-Based Feature Learning | [
"cs.LG"
] | The rapid expansion of the Industrial Internet of Things (IIoT) has significantly advanced digital technologies and interconnected industrial systems, creating substantial opportunities for growth. However, this growth has also heightened the risk of cyberattacks, necessitating robust security measures to protect IIoT ... | {
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2501.15268 | New Evaluation Paradigm for Lexical Simplification | [
"cs.CL"
] | Lexical Simplification (LS) methods use a three-step pipeline: complex word identification, substitute generation, and substitute ranking, each with separate evaluation datasets. We found large language models (LLMs) can simplify sentences directly with a single prompt, bypassing the traditional pipeline. However, exis... | {
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2501.15269 | Mirage in the Eyes: Hallucination Attack on Multi-modal Large Language
Models with Only Attention Sink | [
"cs.LG",
"cs.CR",
"cs.CV"
] | Fusing visual understanding into language generation, Multi-modal Large Language Models (MLLMs) are revolutionizing visual-language applications. Yet, these models are often plagued by the hallucination problem, which involves generating inaccurate objects, attributes, and relationships that do not match the visual con... | {
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2501.15270 | Inductive Biases for Zero-shot Systematic Generalization in
Language-informed Reinforcement Learning | [
"cs.LG",
"cs.AI",
"cs.CL"
] | Sample efficiency and systematic generalization are two long-standing challenges in reinforcement learning. Previous studies have shown that involving natural language along with other observation modalities can improve generalization and sample efficiency due to its compositional and open-ended nature. However, to tra... | {
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2501.15271 | Killing it with Zero-Shot: Adversarially Robust Novelty Detection | [
"cs.LG"
] | Novelty Detection (ND) plays a crucial role in machine learning by identifying new or unseen data during model inference. This capability is especially important for the safe and reliable operation of automated systems. Despite advances in this field, existing techniques often fail to maintain their performance when su... | {
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2501.15272 | Safe and Agile Transportation of Cable-Suspended Payload via Multiple
Aerial Robots | [
"cs.RO"
] | Transporting a heavy payload using multiple aerial robots (MARs) is an efficient manner to extend the load capacity of a single aerial robot. However, existing schemes for the multiple aerial robots transportation system (MARTS) still lack the capability to generate a collision-free and dynamically feasible trajectory ... | {
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2501.15273 | Into the Void: Mapping the Unseen Gaps in High Dimensional Data | [
"cs.LG",
"cs.HC"
] | We present a comprehensive pipeline, augmented by a visual analytics system named ``GapMiner'', that is aimed at exploring and exploiting untapped opportunities within the empty areas of high-dimensional datasets. Our approach begins with an initial dataset and then uses a novel Empty Space Search Algorithm (ESA) to id... | {
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2501.15276 | Exploring the Collaborative Co-Creation Process with AI: A Case Study in
Novice Music Production | [
"cs.HC",
"cs.AI"
] | Artificial intelligence is reshaping creative domains, yet its co-creative processes, especially in group settings with novice users, remain under explored. To bridge this gap, we conducted a case study in a college-level course where nine undergraduate students were tasked with creating three original music tracks usi... | {
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2501.15278 | PIP: Perturbation-based Iterative Pruning for Large Language Models | [
"cs.LG",
"cs.CL"
] | The rapid increase in the parameter counts of Large Language Models (LLMs), reaching billions or even trillions, presents significant challenges for their practical deployment, particularly in resource-constrained environments. To ease this issue, we propose PIP (Perturbation-based Iterative Pruning), a novel double-vi... | {
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2501.15280 | Who's Driving? Game Theoretic Path Risk of AGI Development | [
"cs.AI",
"cs.CY",
"cs.GT"
] | Who controls the development of Artificial General Intelligence (AGI) might matter less than how we handle the fight for control itself. We formalize this "steering wheel problem" as humanity's greatest near-term existential risk may stem not from misaligned AGI, but from the dynamics of competing to develop it. Just a... | {
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2501.15281 | Pre-training a Transformer-Based Generative Model Using a Small Sepedi
Dataset | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Due to the scarcity of data in low-resourced languages, the development of language models for these languages has been very slow. Currently, pre-trained language models have gained popularity in natural language processing, especially, in developing domain-specific models for low-resourced languages. In this study, we... | {
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2501.15282 | AutoG: Towards automatic graph construction from tabular data | [
"cs.LG"
] | Recent years have witnessed significant advancements in graph machine learning (GML), with its applications spanning numerous domains. However, the focus of GML has predominantly been on developing powerful models, often overlooking a crucial initial step: constructing suitable graphs from common data formats, such as ... | {
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2501.15283 | Are Human Interactions Replicable by Generative Agents? A Case Study on
Pronoun Usage in Hierarchical Interactions | [
"cs.CL"
] | As Large Language Models (LLMs) advance in their capabilities, researchers have increasingly employed them for social simulation. In this paper, we investigate whether interactions among LLM agents resemble those of humans. Specifically, we focus on the pronoun usage difference between leaders and non-leaders, examinin... | {
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2501.15286 | Efficient Point Clouds Upsampling via Flow Matching | [
"cs.CV",
"eess.SP"
] | Diffusion models are a powerful framework for tackling ill-posed problems, with recent advancements extending their use to point cloud upsampling. Despite their potential, existing diffusion models struggle with inefficiencies as they map Gaussian noise to real point clouds, overlooking the geometric information inhere... | {
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2501.15288 | A Two-Stage CAE-Based Federated Learning Framework for Efficient Jamming
Detection in 5G Networks | [
"cs.CR",
"cs.LG"
] | Cyber-security for 5G networks is drawing notable attention due to an increase in complex jamming attacks that could target the critical 5G Radio Frequency (RF) domain. These attacks pose a significant risk to heterogeneous network (HetNet) architectures, leading to degradation in network performance. Conventional mach... | {
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2501.15290 | Advanced Real-Time Fraud Detection Using RAG-Based LLMs | [
"cs.CR",
"cs.AI"
] | Artificial Intelligence has become a double edged sword in modern society being both a boon and a bane. While it empowers individuals it also enables malicious actors to perpetrate scams such as fraudulent phone calls and user impersonations. This growing threat necessitates a robust system to protect individuals In th... | {
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2501.15293 | Deep Learning in Early Alzheimer's disease's Detection: A Comprehensive
Survey of Classification, Segmentation, and Feature Extraction Methods | [
"cs.LG"
] | Alzheimers disease is a deadly neurological condition, impairing important memory and brain functions. Alzheimers disease promotes brain shrinkage, ultimately leading to dementia. Dementia diagnosis typically takes 2.8 to 4.4 years after the first clinical indication. Advancements in computing and information technolog... | {
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} |
2501.15296 | You Only Prune Once: Designing Calibration-Free Model Compression With
Policy Learning | [
"cs.CL"
] | The ever-increasing size of large language models (LLMs) presents significant challenges for deployment due to their heavy computational and memory requirements. Current model pruning techniques attempt to alleviate these issues by relying heavily on external calibration datasets to determine which parameters to prune ... | {
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} |
2501.15301 | Separable Computation of Information Measures | [
"cs.IT",
"cs.LG",
"math.IT",
"stat.ML"
] | We study a separable design for computing information measures, where the information measure is computed from learned feature representations instead of raw data. Under mild assumptions on the feature representations, we demonstrate that a class of information measures admit such separable computation, including mutua... | {
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} |
2501.15304 | Music Generation using Human-In-The-Loop Reinforcement Learning | [
"cs.SD",
"cs.AI",
"cs.HC",
"cs.LG",
"eess.AS"
] | This paper presents an approach that combines Human-In-The-Loop Reinforcement Learning (HITL RL) with principles derived from music theory to facilitate real-time generation of musical compositions. HITL RL, previously employed in diverse applications such as modelling humanoid robot mechanics and enhancing language mo... | {
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} |
2501.15305 | Enhancing Disaster Resilience with UAV-Assisted Edge Computing: A
Reinforcement Learning Approach to Managing Heterogeneous Edge Devices | [
"cs.ET",
"cs.AI",
"cs.DC"
] | Edge sensing and computing is rapidly becoming part of intelligent infrastructure architecture leading to operational reliance on such systems in disaster or emergency situations. In such scenarios there is a high chance of power supply failure due to power grid issues, and communication system issues due to base stati... | {
"Other": 1,
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} |
2501.15309 | Investigating the Feasibility of Patch-based Inference for Generalized
Diffusion Priors in Inverse Problems for Medical Images | [
"eess.IV",
"cs.CV",
"cs.LG"
] | Plug-and-play approaches to solving inverse problems such as restoration and super-resolution have recently benefited from Diffusion-based generative priors for natural as well as medical images. However, solutions often use the standard albeit computationally intensive route of training and inferring with the whole im... | {
"Other": 0,
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} |
2501.15310 | The Multicultural Medical Assistant: Can LLMs Improve Medical ASR Errors
Across Borders? | [
"cs.CL",
"cs.SD",
"eess.AS"
] | The global adoption of Large Language Models (LLMs) in healthcare shows promise to enhance clinical workflows and improve patient outcomes. However, Automatic Speech Recognition (ASR) errors in critical medical terms remain a significant challenge. These errors can compromise patient care and safety if not detected. Th... | {
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"cs.SD": 1,
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"cs.SY": 0
} |
2501.15316 | ToMoE: Converting Dense Large Language Models to Mixture-of-Experts
through Dynamic Structural Pruning | [
"cs.LG",
"cs.CL"
] | Large Language Models (LLMs) have demonstrated remarkable abilities in tackling a wide range of complex tasks. However, their huge computational and memory costs raise significant challenges in deploying these models on resource-constrained devices or efficiently serving them. Prior approaches have attempted to allevia... | {
"Other": 0,
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} |
2501.15318 | A Post-Processing-Based Fair Federated Learning Framework | [
"cs.LG",
"cs.AI",
"cs.CY"
] | Federated Learning (FL) allows collaborative model training among distributed parties without pooling local datasets at a central server. However, the distributed nature of FL poses challenges in training fair federated learning models. The existing techniques are often limited in offering fairness flexibility to clien... | {
"Other": 0,
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} |
2501.15319 | PSO and the Traveling Salesman Problem: An Intelligent Optimization
Approach | [
"cs.NE",
"math.OC"
] | The Traveling Salesman Problem (TSP) is a well-known combinatorial optimization problem that aims to find the shortest possible route that visits each city exactly once and returns to the starting point. This paper explores the application of Particle Swarm Optimization (PSO), a population-based optimization algorithm,... | {
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"cs.NE": 1,
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"cs.SY": 0
} |
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