id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2412.11185 | Transliterated Zero-Shot Domain Adaptation for Automatic Speech
Recognition | [
"eess.AS",
"cs.CL",
"cs.SD"
] | The performance of automatic speech recognition models often degenerates on domains not covered by the training data. Domain adaptation can address this issue, assuming the availability of the target domain data in the target language. However, such assumption does not stand in many real-world applications. To make dom... | {
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2412.11186 | Efficient Quantization-Aware Training on Segment Anything Model in
Medical Images and Its Deployment | [
"cs.CV",
"cs.AI"
] | Medical image segmentation is a critical component of clinical practice, and the state-of-the-art MedSAM model has significantly advanced this field. Nevertheless, critiques highlight that MedSAM demands substantial computational resources during inference. To address this issue, the CVPR 2024 MedSAM on Laptop Challeng... | {
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2412.11187 | Analyzing the Attention Heads for Pronoun Disambiguation in
Context-aware Machine Translation Models | [
"cs.CL",
"cs.AI",
"cs.LG"
] | In this paper, we investigate the role of attention heads in Context-aware Machine Translation models for pronoun disambiguation in the English-to-German and English-to-French language directions. We analyze their influence by both observing and modifying the attention scores corresponding to the plausible relations th... | {
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2412.11189 | Leveraging Large Language Models for Active Merchant Non-player
Characters | [
"cs.AI",
"cs.CL"
] | We highlight two significant issues leading to the passivity of current merchant non-player characters (NPCs): pricing and communication. While immersive interactions have been a focus, negotiations between merchant NPCs and players on item prices have not received sufficient attention. First, we define passive pricing... | {
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2412.11192 | From Votes to Volatility Predicting the Stock Market on Election Day | [
"q-fin.CP",
"cs.AI"
] | Stock market forecasting has been a topic of extensive research, aiming to provide investors with optimal stock recommendations for higher returns. In recent years, this field has gained even more attention due to the widespread adoption of deep learning models. While these models have achieved impressive accuracy in p... | {
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2412.11193 | Light-T2M: A Lightweight and Fast Model for Text-to-motion Generation | [
"cs.CV"
] | Despite the significant role text-to-motion (T2M) generation plays across various applications, current methods involve a large number of parameters and suffer from slow inference speeds, leading to high usage costs. To address this, we aim to design a lightweight model to reduce usage costs. First, unlike existing wor... | {
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2412.11194 | SoK: On Closing the Applicability Gap in Automated Vulnerability
Detection | [
"cs.SE",
"cs.AI"
] | The frequent discovery of security vulnerabilities in both open-source and proprietary software underscores the urgent need for earlier detection during the development lifecycle. Initiatives such as DARPA's Artificial Intelligence Cyber Challenge (AIxCC) aim to accelerate Automated Vulnerability Detection (AVD), seeki... | {
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2412.11196 | Drawing the Line: Enhancing Trustworthiness of MLLMs Through the Power
of Refusal | [
"cs.CL",
"cs.CV"
] | Multimodal large language models (MLLMs) excel at multimodal perception and understanding, yet their tendency to generate hallucinated or inaccurate responses undermines their trustworthiness. Existing methods have largely overlooked the importance of refusal responses as a means of enhancing MLLMs reliability. To brid... | {
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2412.11198 | GEM: A Generalizable Ego-Vision Multimodal World Model for Fine-Grained
Ego-Motion, Object Dynamics, and Scene Composition Control | [
"cs.CV"
] | We present GEM, a Generalizable Ego-vision Multimodal world model that predicts future frames using a reference frame, sparse features, human poses, and ego-trajectories. Hence, our model has precise control over object dynamics, ego-agent motion and human poses. GEM generates paired RGB and depth outputs for richer sp... | {
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2412.11203 | Task-Oriented Dialog Systems for the Senegalese Wolof Language | [
"cs.CL",
"cs.AI",
"cs.HC",
"cs.IR"
] | In recent years, we are seeing considerable interest in conversational agents with the rise of large language models (LLMs). Although they offer considerable advantages, LLMs also present significant risks, such as hallucination, which hinder their widespread deployment in industry. Moreover, low-resource languages suc... | {
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2412.11205 | Concept Learning in the Wild: Towards Algorithmic Understanding of
Neural Networks | [
"cs.LG"
] | Explainable AI (XAI) methods typically focus on identifying essential input features or more abstract concepts for tasks like image or text classification. However, for algorithmic tasks like combinatorial optimization, these concepts may depend not only on the input but also on the current state of the network, like i... | {
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2412.11207 | ProFe: Communication-Efficient Decentralized Federated Learning via
Distillation and Prototypes | [
"cs.LG",
"cs.AI",
"cs.DC",
"cs.NI"
] | Decentralized Federated Learning (DFL) trains models in a collaborative and privacy-preserving manner while removing model centralization risks and improving communication bottlenecks. However, DFL faces challenges in efficient communication management and model aggregation within decentralized environments, especially... | {
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2412.11210 | ViPOcc: Leveraging Visual Priors from Vision Foundation Models for
Single-View 3D Occupancy Prediction | [
"cs.CV"
] | Inferring the 3D structure of a scene from a single image is an ill-posed and challenging problem in the field of vision-centric autonomous driving. Existing methods usually employ neural radiance fields to produce voxelized 3D occupancy, lacking instance-level semantic reasoning and temporal photometric consistency. I... | {
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2412.11211 | Deep Learning-based Approaches for State Space Models: A Selective
Review | [
"stat.ML",
"cs.LG",
"stat.OT"
] | State-space models (SSMs) offer a powerful framework for dynamical system analysis, wherein the temporal dynamics of the system are assumed to be captured through the evolution of the latent states, which govern the values of the observations. This paper provides a selective review of recent advancements in deep neural... | {
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2412.11212 | Practical IMT and EESS Spectrum Sharing in the 7 to 8 GHz Band | [
"eess.SP",
"cs.CE"
] | The 7.3 GHz (350 MHz bandwidth) Earth Observation Satellite (EOS) band, while not protected, is used for Passive Sea Surface Temperature (P-SST) measurements that provide important data for weather forecasts, coastal disaster prevention, climate modeling, and oceanographic research. The full 7 GHz band (7.125 to 8.4 GH... | {
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2412.11214 | Image Forgery Localization with State Space Models | [
"cs.CV"
] | Pixel dependency modeling from tampered images is pivotal for image forgery localization. Current approaches predominantly rely on Convolutional Neural Networks (CNNs) or Transformer-based models, which often either lack sufficient receptive fields or entail significant computational overheads. Recently, State Space Mo... | {
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2412.11215 | Neural Port-Hamiltonian Differential Algebraic Equations for
Compositional Learning of Electrical Networks | [
"cs.LG",
"cs.AI",
"cs.SY",
"eess.SY"
] | We develop compositional learning algorithms for coupled dynamical systems. While deep learning has proven effective at modeling complex relationships from data, compositional couplings between system components typically introduce algebraic constraints on state variables, posing challenges to many existing data-driven... | {
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2412.11216 | Distribution-Consistency-Guided Multi-modal Hashing | [
"cs.CV",
"cs.AI",
"cs.IR"
] | Multi-modal hashing methods have gained popularity due to their fast speed and low storage requirements. Among them, the supervised methods demonstrate better performance by utilizing labels as supervisory signals compared with unsupervised methods. Currently, for almost all supervised multi-modal hashing methods, ther... | {
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2412.11224 | GenLit: Reformulating Single-Image Relighting as Video Generation | [
"cs.CV",
"cs.GR"
] | Manipulating the illumination within a single image represents a fundamental challenge in computer vision and graphics. This problem has been traditionally addressed using inverse rendering techniques, which require explicit 3D asset reconstruction and costly ray tracing simulations. Meanwhile, recent advancements in v... | {
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2412.11228 | Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition | [
"cs.CV",
"cs.AI",
"cs.LG"
] | This paper presents a comprehensive exploration of the phenomenon of data redundancy in video understanding, with the aim to improve computational efficiency. Our investigation commences with an examination of spatial redundancy, which refers to the observation that the most informative region in each video frame usual... | {
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2412.11231 | Smaller Language Models Are Better Instruction Evolvers | [
"cs.CL"
] | Instruction tuning has been widely used to unleash the complete potential of large language models. Notably, complex and diverse instructions are of significant importance as they can effectively align models with various downstream tasks. However, current approaches to constructing large-scale instructions predominant... | {
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2412.11236 | Logarithmic Positional Partition Interval Encoding | [
"cs.DS",
"cs.IT",
"eess.SP",
"math.IT"
] | One requirement of maintaining digital information is storage. With the latest advances in the digital world, new emerging media types have required even more storage space to be kept than before. In fact, in many cases it is required to have larger amounts of storage to keep up with protocols that support more types o... | {
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2412.11237 | On the Generalizability of Iterative Patch Selection for
Memory-Efficient High-Resolution Image Classification | [
"cs.CV"
] | Classifying large images with small or tiny regions of interest (ROI) is challenging due to computational and memory constraints. Weakly supervised memory-efficient patch selectors have achieved results comparable with strongly supervised methods. However, low signal-to-noise ratios and low entropy attention still caus... | {
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2412.11239 | Learning Set Functions with Implicit Differentiation | [
"cs.LG",
"cs.AI"
] | Ou et al. (2022) introduce the problem of learning set functions from data generated by a so-called optimal subset oracle. Their approach approximates the underlying utility function with an energy-based model, whose parameters are estimated via mean-field variational inference. Ou et al. (2022) show this reduces to fi... | {
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2412.11241 | Volumetric Mapping with Panoptic Refinement via Kernel Density
Estimation for Mobile Robots | [
"cs.RO",
"cs.CV"
] | Reconstructing three-dimensional (3D) scenes with semantic understanding is vital in many robotic applications. Robots need to identify which objects, along with their positions and shapes, to manipulate them precisely with given tasks. Mobile robots, especially, usually use lightweight networks to segment objects on R... | {
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2412.11242 | TrimLLM: Progressive Layer Dropping for Domain-Specific LLMs | [
"cs.LG",
"cs.AI",
"cs.CL"
] | Specializing large language models (LLMs) for local deployment in domain-specific use cases is necessary for strong performance while meeting latency and privacy constraints. However, conventional task-specific adaptation approaches do not show simultaneous memory saving and inference speedup at deployment time. Practi... | {
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2412.11245 | Transformer-Based Bearing Fault Detection using Temporal Decomposition
Attention Mechanism | [
"cs.LG",
"cs.AI",
"eess.SP"
] | Bearing fault detection is a critical task in predictive maintenance, where accurate and timely fault identification can prevent costly downtime and equipment damage. Traditional attention mechanisms in Transformer neural networks often struggle to capture the complex temporal patterns in bearing vibration data, leadin... | {
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2412.11248 | Multimodal Class-aware Semantic Enhancement Network for Audio-Visual
Video Parsing | [
"cs.CV",
"cs.MM"
] | The Audio-Visual Video Parsing task aims to recognize and temporally localize all events occurring in either the audio or visual stream, or both. Capturing accurate event semantics for each audio/visual segment is vital. Prior works directly utilize the extracted holistic audio and visual features for intra- and cross-... | {
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2412.11250 | Beyond Discrete Personas: Personality Modeling Through Journal Intensive
Conversations | [
"cs.CL",
"cs.AI"
] | Large Language Models (LLMs) have significantly improved personalized conversational capabilities. However, existing datasets like Persona Chat, Synthetic Persona Chat, and Blended Skill Talk rely on static, predefined personas. This approach often results in dialogues that fail to capture human personalities' fluid an... | {
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2412.11251 | Wasserstein Bounds for generative diffusion models with Gaussian tail
targets | [
"cs.LG",
"cs.NA",
"math.AP",
"math.NA"
] | We present an estimate of the Wasserstein distance between the data distribution and the generation of score-based generative models, assuming an $\epsilon$-accurate approximation of the score and a Gaussian-type tail behavior of the data distribution. The complexity bound in dimension is $O(\sqrt{d})$, with a logarith... | {
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2412.11253 | Are Expressive Models Truly Necessary for Offline RL? | [
"cs.LG",
"cs.AI"
] | Among various branches of offline reinforcement learning (RL) methods, goal-conditioned supervised learning (GCSL) has gained increasing popularity as it formulates the offline RL problem as a sequential modeling task, therefore bypassing the notoriously difficult credit assignment challenge of value learning in conven... | {
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2412.11255 | Do Tutors Learn from Equity Training and Can Generative AI Assess It? | [
"cs.HC",
"cs.AI"
] | Equity is a core concern of learning analytics. However, applications that teach and assess equity skills, particularly at scale are lacking, often due to barriers in evaluating language. Advances in generative AI via large language models (LLMs) are being used in a wide range of applications, with this present work as... | {
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2412.11257 | Prediction-Enhanced Monte Carlo: A Machine Learning View on Control
Variate | [
"stat.ML",
"cs.CE",
"cs.LG",
"q-fin.PR"
] | Despite being an essential tool across engineering and finance, Monte Carlo simulation can be computationally intensive, especially in large-scale, path-dependent problems that hinder straightforward parallelization. A natural alternative is to replace simulation with machine learning or surrogate prediction, though th... | {
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2412.11258 | GaussianProperty: Integrating Physical Properties to 3D Gaussians with
LMMs | [
"cs.RO",
"cs.AI",
"cs.CV"
] | Estimating physical properties for visual data is a crucial task in computer vision, graphics, and robotics, underpinning applications such as augmented reality, physical simulation, and robotic grasping. However, this area remains under-explored due to the inherent ambiguities in physical property estimation. To addre... | {
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2412.11261 | CATER: Leveraging LLM to Pioneer a Multidimensional,
Reference-Independent Paradigm in Translation Quality Evaluation | [
"cs.CL",
"cs.AI"
] | This paper introduces the Comprehensive AI-assisted Translation Edit Ratio (CATER), a novel and fully prompt-driven framework for evaluating machine translation (MT) quality. Leveraging large language models (LLMs) via a carefully designed prompt-based protocol, CATER expands beyond traditional reference-bound metrics,... | {
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2412.11266 | Bayesian inference of mean velocity fields and turbulence models from
flow MRI | [
"physics.flu-dyn",
"cs.LG",
"math.OC"
] | We solve a Bayesian inverse Reynolds-averaged Navier-Stokes (RANS) problem that assimilates mean flow data by jointly reconstructing the mean flow field and learning its unknown RANS parameters. We devise an algorithm that learns the most likely parameters of an algebraic effective viscosity model, and estimates their ... | {
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2412.11270 | Monte Carlo Tree Search with Spectral Expansion for Planning with
Dynamical Systems | [
"cs.RO"
] | The ability of a robot to plan complex behaviors with real-time computation, rather than adhering to predesigned or offline-learned routines, alleviates the need for specialized algorithms or training for each problem instance. Monte Carlo Tree Search is a powerful planning algorithm that strategically explores simulat... | {
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2412.11275 | Adaptive Visual Perception for Robotic Construction Process: A
Multi-Robot Coordination Framework | [
"cs.RO"
] | Construction robots operate in unstructured construction sites, where effective visual perception is crucial for ensuring safe and seamless operations. However, construction robots often handle large elements and perform tasks across expansive areas, resulting in occluded views from onboard cameras and necessitating th... | {
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2412.11276 | Wearable Accelerometer Foundation Models for Health via Knowledge
Distillation | [
"cs.LG",
"cs.AI",
"eess.SP"
] | Modern wearable devices can conveniently record various biosignals in the many different environments of daily living, enabling a rich view of individual health. However, not all biosignals are the same: high-fidelity biosignals, such as photoplethysmogram (PPG), contain more physiological information, but require opti... | {
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2412.11277 | Macro2Micro: Cross-modal Magnetic Resonance Imaging Synthesis Leveraging
Multi-scale Brain Structures | [
"eess.IV",
"cs.AI",
"cs.CV"
] | Spanning multiple scales-from macroscopic anatomy down to intricate microscopic architecture-the human brain exemplifies a complex system that demands integrated approaches to fully understand its complexity. Yet, mapping nonlinear relationships between these scales remains challenging due to technical limitations and ... | {
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2412.11279 | VividFace: A Diffusion-Based Hybrid Framework for High-Fidelity Video
Face Swapping | [
"cs.CV",
"cs.AI",
"cs.GR"
] | Video face swapping is becoming increasingly popular across various applications, yet existing methods primarily focus on static images and struggle with video face swapping because of temporal consistency and complex scenarios. In this paper, we present the first diffusion-based framework specifically designed for vid... | {
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2412.11281 | Multi-robot workspace design and motion planning for package sorting | [
"cs.RO"
] | Robotic systems are routinely used in the logistics industry to enhance operational efficiency, but the design of robot workspaces remains a complex and manual task, which limits the system's flexibility to changing demands. This paper aims to automate robot workspace design by proposing a computational framework to ge... | {
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2412.11284 | Learning Normal Flow Directly From Event Neighborhoods | [
"cs.CV"
] | Event-based motion field estimation is an important task. However, current optical flow methods face challenges: learning-based approaches, often frame-based and relying on CNNs, lack cross-domain transferability, while model-based methods, though more robust, are less accurate. To address the limitations of optical fl... | {
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2412.11286 | Detecting Daily Living Gait Amid Huntington's Disease Chorea using a
Foundation Deep Learning Model | [
"cs.CV",
"cs.AI"
] | Wearable sensors offer a non-invasive way to collect physical activity (PA) data, with walking as a key component. Existing models often struggle to detect gait bouts in individuals with neurodegenerative diseases (NDDs) involving involuntary movements. We developed J-Net, a deep learning model inspired by U-Net, which... | {
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2412.11292 | Grassmannian Geometry Meets Dynamic Mode Decomposition in DMD-GEN: A New
Metric for Mode Collapse in Time Series Generative Models | [
"cs.LG"
] | Generative models like Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) often fail to capture the full diversity of their training data, leading to mode collapse. While this issue is well-explored in image generation, it remains underinvestigated for time series data. We introduce a new defini... | {
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2412.11293 | A Comparative Study on Dynamic Graph Embedding based on Mamba and
Transformers | [
"cs.LG",
"cs.AI"
] | Dynamic graph embedding has emerged as an important technique for modeling complex time-evolving networks across diverse domains. While transformer-based models have shown promise in capturing long-range dependencies in temporal graph data, they face scalability challenges due to quadratic computational complexity. Thi... | {
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2412.11299 | How not to Stitch Representations to Measure Similarity: Task Loss
Matching versus Direct Matching | [
"cs.LG",
"stat.ML"
] | Measuring the similarity of the internal representations of deep neural networks is an important and challenging problem. Model stitching has been proposed as a possible approach, where two half-networks are connected by mapping the output of the first half-network to the input of the second one. The representations ar... | {
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2412.11301 | Semi-Implicit Neural Ordinary Differential Equations | [
"cs.LG",
"cs.AI"
] | Classical neural ODEs trained with explicit methods are intrinsically limited by stability, crippling their efficiency and robustness for stiff learning problems that are common in graph learning and scientific machine learning. We present a semi-implicit neural ODE approach that exploits the partitionable structure of... | {
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2412.11302 | Sequence-Level Leakage Risk of Training Data in Large Language Models | [
"cs.CL",
"cs.LG"
] | This work performs an analysis of sequence-level probabilities for quantifying the of risk training data extraction from Large Language Models (LLMs). Per-sequence extraction probabilities provide finer-grained information than has been studied in prior work. We re-analyze the effects of decoding schemes, model sizes, ... | {
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2412.11303 | Regularized Dikin Walks for Sampling Truncated Logconcave Measures,
Mixed Isoperimetry and Beyond Worst-Case Analysis | [
"cs.DS",
"cs.LG",
"stat.CO",
"stat.ML"
] | We study the problem of drawing samples from a logconcave distribution truncated on a polytope, motivated by computational challenges in Bayesian statistical models with indicator variables, such as probit regression. Building on interior point methods and the Dikin walk for sampling from uniform distributions, we anal... | {
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2412.11304 | An Empirical Study of Fault Localisation Techniques for Deep Learning | [
"cs.SE",
"cs.AI",
"cs.LG"
] | With the increased popularity of Deep Neural Networks (DNNs), increases also the need for tools to assist developers in the DNN implementation, testing and debugging process. Several approaches have been proposed that automatically analyse and localise potential faults in DNNs under test. In this work, we evaluate and ... | {
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2412.11306 | Unimodal and Multimodal Static Facial Expression Recognition for Virtual
Reality Users with EmoHeVRDB | [
"cs.CV"
] | In this study, we explored the potential of utilizing Facial Expression Activations (FEAs) captured via the Meta Quest Pro Virtual Reality (VR) headset for Facial Expression Recognition (FER) in VR settings. Leveraging the EmojiHeroVR Database (EmoHeVRDB), we compared several unimodal approaches and achieved up to 73.0... | {
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2412.11308 | datadriftR: An R Package for Concept Drift Detection in Predictive
Models | [
"stat.ML",
"cs.LG"
] | Predictive models often face performance degradation due to evolving data distributions, a phenomenon known as data drift. Among its forms, concept drift, where the relationship between explanatory variables and the response variable changes, is particularly challenging to detect and adapt to. Traditional drift detecti... | {
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2412.11314 | Reliable, Reproducible, and Really Fast Leaderboards with Evalica | [
"cs.CL"
] | The rapid advancement of natural language processing (NLP) technologies, such as instruction-tuned large language models (LLMs), urges the development of modern evaluation protocols with human and machine feedback. We introduce Evalica, an open-source toolkit that facilitates the creation of reliable and reproducible m... | {
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2412.11317 | RoLargeSum: A Large Dialect-Aware Romanian News Dataset for Summary,
Headline, and Keyword Generation | [
"cs.CL"
] | Using supervised automatic summarisation methods requires sufficient corpora that include pairs of documents and their summaries. Similarly to many tasks in natural language processing, most of the datasets available for summarization are in English, posing challenges for developing summarization models in other langua... | {
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2412.11318 | Generics are puzzling. Can language models find the missing piece? | [
"cs.CL",
"cs.AI"
] | Generic sentences express generalisations about the world without explicit quantification. Although generics are central to everyday communication, building a precise semantic framework has proven difficult, in part because speakers use generics to generalise properties with widely different statistical prevalence. In ... | {
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2412.11325 | Sonicmesh: Enhancing 3D Human Mesh Reconstruction in Vision-Impaired
Environments With Acoustic Signals | [
"cs.CV",
"cs.SD",
"eess.AS"
] | 3D Human Mesh Reconstruction (HMR) from 2D RGB images faces challenges in environments with poor lighting, privacy concerns, or occlusions. These weaknesses of RGB imaging can be complemented by acoustic signals, which are widely available, easy to deploy, and capable of penetrating obstacles. However, no existing meth... | {
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2412.11333 | Segment-Level Diffusion: A Framework for Controllable Long-Form
Generation with Diffusion Language Models | [
"cs.CL",
"cs.AI"
] | Diffusion models have shown promise in text generation but often struggle with generating long, coherent, and contextually accurate text. Token-level diffusion overlooks word-order dependencies and enforces short output windows, while passage-level diffusion struggles with learning robust representation for long-form t... | {
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2412.11337 | Modality-Driven Design for Multi-Step Dexterous Manipulation: Insights
from Neuroscience | [
"cs.RO",
"cs.AI",
"cs.CV"
] | Multi-step dexterous manipulation is a fundamental skill in household scenarios, yet remains an underexplored area in robotics. This paper proposes a modular approach, where each step of the manipulation process is addressed with dedicated policies based on effective modality input, rather than relying on a single end-... | {
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2412.11341 | Coupling-based Convergence Diagnostic and Stepsize Scheme for Stochastic
Gradient Descent | [
"cs.LG",
"math.OC",
"stat.ML"
] | The convergence behavior of Stochastic Gradient Descent (SGD) crucially depends on the stepsize configuration. When using a constant stepsize, the SGD iterates form a Markov chain, enjoying fast convergence during the initial transient phase. However, when reaching stationarity, the iterates oscillate around the optimu... | {
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2412.11342 | One-Shot Multilingual Font Generation Via ViT | [
"cs.CV"
] | Font design poses unique challenges for logographic languages like Chinese, Japanese, and Korean (CJK), where thousands of unique characters must be individually crafted. This paper introduces a novel Vision Transformer (ViT)-based model for multi-language font generation, effectively addressing the complexities of bot... | {
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2412.11343 | Temporal Logic Control for Nonlinear Stochastic Systems Under Unknown
Disturbances | [
"eess.SY",
"cs.SY"
] | In this paper, we present a novel framework to synthesize robust strategies for discrete-time nonlinear systems with random disturbances that are unknown, against temporal logic specifications. The proposed framework is data-driven and abstraction-based: leveraging observations of the system, our approach learns a high... | {
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2412.11344 | Can AI Extract Antecedent Factors of Human Trust in AI? An Application
of Information Extraction for Scientific Literature in Behavioural and
Computer Sciences | [
"cs.CL",
"cs.AI"
] | Information extraction from the scientific literature is one of the main techniques to transform unstructured knowledge hidden in the text into structured data which can then be used for decision-making in down-stream tasks. One such area is Trust in AI, where factors contributing to human trust in artificial intellige... | {
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2412.11350 | Deep Random Features for Scalable Interpolation of Spatiotemporal Data | [
"cs.LG",
"stat.ML"
] | The rapid growth of earth observation systems calls for a scalable approach to interpolate remote-sensing observations. These methods in principle, should acquire more information about the observed field as data grows. Gaussian processes (GPs) are candidate model choices for interpolation. However, due to their poor s... | {
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2412.11356 | The Stabilizer Bootstrap of Quantum Machine Learning with up to 10000
qubits | [
"quant-ph",
"cs.AI",
"cs.LG"
] | Quantum machine learning is considered one of the flagship applications of quantum computers, where variational quantum circuits could be the leading paradigm both in the near-term quantum devices and the early fault-tolerant quantum computers. However, it is not clear how to identify the regime of quantum advantages f... | {
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2412.11360 | Visual IRL for Human-Like Robotic Manipulation | [
"cs.RO",
"cs.AI",
"cs.CV"
] | We present a novel method for collaborative robots (cobots) to learn manipulation tasks and perform them in a human-like manner. Our method falls under the learn-from-observation (LfO) paradigm, where robots learn to perform tasks by observing human actions, which facilitates quicker integration into industrial setting... | {
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2412.11362 | VRVVC: Variable-Rate NeRF-Based Volumetric Video Compression | [
"eess.IV",
"cs.CV"
] | Neural Radiance Field (NeRF)-based volumetric video has revolutionized visual media by delivering photorealistic Free-Viewpoint Video (FVV) experiences that provide audiences with unprecedented immersion and interactivity. However, the substantial data volumes pose significant challenges for storage and transmission. E... | {
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2412.11364 | Individual Bus Trip Chain Prediction and Pattern Identification
Considering Similarities | [
"cs.LG",
"cs.AI"
] | Predicting future bus trip chains for an existing user is of great significance for operators of public transit systems. Existing methods always treat this task as a time-series prediction problem, but the 1-dimensional time series structure cannot express the complex relationship between trips. To better capture the i... | {
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2412.11365 | BiM-VFI: directional Motion Field-Guided Frame Interpolation for Video
with Non-uniform Motions | [
"cs.CV"
] | Existing Video Frame interpolation (VFI) models tend to suffer from time-to-location ambiguity when trained with video of non-uniform motions, such as accelerating, decelerating, and changing directions, which often yield blurred interpolated frames. In this paper, we propose (i) a novel motion description map, Bidirec... | {
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2412.11373 | Codenames as a Benchmark for Large Language Models | [
"cs.AI",
"cs.CL"
] | In this paper, we propose the use of the popular word-based board game Codenames as a suitable benchmark for evaluating the reasoning capabilities of Large Language Models (LLMs). Codenames presents a highly interesting challenge for achieving successful AI performance, requiring both a sophisticated understanding of l... | {
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2412.11375 | Text and Image Are Mutually Beneficial: Enhancing Training-Free Few-Shot
Classification with CLIP | [
"cs.CV"
] | Contrastive Language-Image Pretraining (CLIP) has been widely used in vision tasks. Notably, CLIP has demonstrated promising performance in few-shot learning (FSL). However, existing CLIP-based methods in training-free FSL (i.e., without the requirement of additional training) mainly learn different modalities independ... | {
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2412.11376 | ChatTime: A Unified Multimodal Time Series Foundation Model Bridging
Numerical and Textual Data | [
"cs.CL",
"cs.LG"
] | Human experts typically integrate numerical and textual multimodal information to analyze time series. However, most traditional deep learning predictors rely solely on unimodal numerical data, using a fixed-length window for training and prediction on a single dataset, and cannot adapt to different scenarios. The powe... | {
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2412.11377 | Improving Automatic Fetal Biometry Measurement with Swoosh Activation
Function | [
"eess.IV",
"cs.AI",
"cs.CV"
] | The measurement of fetal thalamus diameter (FTD) and fetal head circumference (FHC) are crucial in identifying abnormal fetal thalamus development as it may lead to certain neuropsychiatric disorders in later life. However, manual measurements from 2D-US images are laborious, prone to high inter-observer variability, a... | {
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2412.11378 | FinLoRA: Finetuning Quantized Financial Large Language Models Using
Low-Rank Adaptation | [
"cs.LG"
] | Finetuned large language models (LLMs) have shown remarkable performance in financial tasks, such as sentiment analysis and information retrieval. Due to privacy concerns, finetuning and deploying Financial LLMs (FinLLMs) locally are crucial for institutions. However, finetuning FinLLMs poses challenges including GPU m... | {
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2412.11379 | Controllable Distortion-Perception Tradeoff Through Latent Diffusion for
Neural Image Compression | [
"eess.IV",
"cs.CV"
] | Neural image compression often faces a challenging trade-off among rate, distortion and perception. While most existing methods typically focus on either achieving high pixel-level fidelity or optimizing for perceptual metrics, we propose a novel approach that simultaneously addresses both aspects for a fixed neural im... | {
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2412.11380 | Relation-Guided Adversarial Learning for Data-free Knowledge Transfer | [
"cs.CV"
] | Data-free knowledge distillation transfers knowledge by recovering training data from a pre-trained model. Despite the recent success of seeking global data diversity, the diversity within each class and the similarity among different classes are largely overlooked, resulting in data homogeneity and limited performance... | {
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2412.11381 | Adapting Segment Anything Model (SAM) to Experimental Datasets via
Fine-Tuning on GAN-based Simulation: A Case Study in Additive Manufacturing | [
"cs.CV",
"cs.AI",
"eess.IV"
] | Industrial X-ray computed tomography (XCT) is a powerful tool for non-destructive characterization of materials and manufactured components. XCT commonly accompanied by advanced image analysis and computer vision algorithms to extract relevant information from the images. Traditional computer vision models often strugg... | {
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2412.11385 | Why Does ChatGPT "Delve" So Much? Exploring the Sources of Lexical
Overrepresentation in Large Language Models | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Scientific English is currently undergoing rapid change, with words like "delve," "intricate," and "underscore" appearing far more frequently than just a few years ago. It is widely assumed that scientists' use of large language models (LLMs) is responsible for such trends. We develop a formal, transferable method to c... | {
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2412.11387 | How Can LLMs and Knowledge Graphs Contribute to Robot Safety? A Few-Shot
Learning Approach | [
"cs.RO",
"cs.AI"
] | Large Language Models (LLMs) are transforming the robotics domain by enabling robots to comprehend and execute natural language instructions. The cornerstone benefits of LLM include processing textual data from technical manuals, instructions, academic papers, and user queries based on the knowledge provided. However, ... | {
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2412.11388 | INTERACT: Enabling Interactive, Question-Driven Learning in Large
Language Models | [
"cs.CL"
] | Large language models (LLMs) excel at answering questions but remain passive learners--absorbing static data without the ability to question and refine knowledge. This paper explores how LLMs can transition to interactive, question-driven learning through student-teacher dialogues. We introduce INTERACT (INTEReractive ... | {
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2412.11390 | Accurate, Robust and Privacy-Preserving Brain-Computer Interface
Decoding | [
"cs.HC",
"cs.LG",
"eess.SP"
] | An electroencephalogram (EEG) based brain-computer interface (BCI) enables direct communication between the brain and external devices. However, EEG-based BCIs face at least three major challenges in real-world applications: data scarcity and individual differences, adversarial vulnerability, and data privacy. While pr... | {
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2412.11391 | Temporal Contrastive Learning for Video Temporal Reasoning in Large
Vision-Language Models | [
"cs.CV"
] | Temporal reasoning is a critical challenge in video-language understanding, as it requires models to align semantic concepts consistently across time. While existing large vision-language models (LVLMs) and large language models (LLMs) excel at static tasks, they struggle to capture dynamic interactions and temporal de... | {
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2412.11393 | STDHL: Spatio-Temporal Dynamic Hypergraph Learning for Wind Power
Forecasting | [
"cs.LG",
"eess.SP"
] | Leveraging spatio-temporal correlations among wind farms can significantly enhance the accuracy of ultra-short-term wind power forecasting. However, the complex and dynamic nature of these correlations presents significant modeling challenges. To address this, we propose a spatio-temporal dynamic hypergraph learning (S... | {
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2412.11395 | Depth-Centric Dehazing and Depth-Estimation from Real-World Hazy Driving
Video | [
"cs.CV"
] | In this paper, we study the challenging problem of simultaneously removing haze and estimating depth from real monocular hazy videos. These tasks are inherently complementary: enhanced depth estimation improves dehazing via the atmospheric scattering model (ASM), while superior dehazing contributes to more accurate dep... | {
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2412.11396 | Leveraging Retrieval-Augmented Tags for Large Vision-Language
Understanding in Complex Scenes | [
"cs.CV"
] | Object-aware reasoning in vision-language tasks poses significant challenges for current models, particularly in handling unseen objects, reducing hallucinations, and capturing fine-grained relationships in complex visual scenes. To address these limitations, we propose the Vision-Aware Retrieval-Augmented Prompting (V... | {
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2412.11399 | Quantization of Climate Change Impacts on Renewable Energy Generation
Capacity: A Super-Resolution Recurrent Diffusion Model | [
"cs.LG",
"eess.SP"
] | Driven by global climate change and the ongoing energy transition, the coupling between power supply capabilities and meteorological factors has become increasingly significant. Over the long term, accurately quantifying the power generation capacity of renewable energy under the influence of climate change is essentia... | {
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2412.11400 | Scaled Conjugate Gradient Method for Nonconvex Optimization in Deep
Neural Networks | [
"cs.LG",
"stat.ML"
] | A scaled conjugate gradient method that accelerates existing adaptive methods utilizing stochastic gradients is proposed for solving nonconvex optimization problems with deep neural networks. It is shown theoretically that, whether with constant or diminishing learning rates, the proposed method can obtain a stationary... | {
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2412.11402 | Modeling Inter-Intra Heterogeneity for Graph Federated Learning | [
"cs.LG"
] | Heterogeneity is a fundamental and challenging issue in federated learning, especially for the graph data due to the complex relationships among the graph nodes. To deal with the heterogeneity, lots of existing methods perform the weighted federation based on their calculated similarities between pairwise clients (i.e.... | {
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2412.11403 | Formulations and scalability of neural network surrogates in nonlinear
optimization problems | [
"cs.LG",
"math.OC"
] | We compare full-space, reduced-space, and gray-box formulations for representing trained neural networks in nonlinear constrained optimization problems. We test these formulations on a transient stability-constrained, security-constrained alternating current optimal power flow (SCOPF) problem where the transient stabil... | {
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2412.11404 | Attention with Dependency Parsing Augmentation for Fine-Grained
Attribution | [
"cs.CL",
"cs.AI"
] | To assist humans in efficiently validating RAG-generated content, developing a fine-grained attribution mechanism that provides supporting evidence from retrieved documents for every answer span is essential. Existing fine-grained attribution methods rely on model-internal similarity metrics between responses and docum... | {
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} |
2412.11407 | An Enhanced Classification Method Based on Adaptive Multi-Scale Fusion
for Long-tailed Multispectral Point Clouds | [
"cs.CV",
"eess.IV"
] | Multispectral point cloud (MPC) captures 3D spatial-spectral information from the observed scene, which can be used for scene understanding and has a wide range of applications. However, most of the existing classification methods were extensively tested on indoor datasets, and when applied to outdoor datasets they sti... | {
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} |
2412.11408 | Federated Domain Generalization with Label Smoothing and Balanced
Decentralized Training | [
"cs.LG",
"cs.AI"
] | In this paper, we propose a novel approach, Federated Domain Generalization with Label Smoothing and Balanced Decentralized Training (FedSB), to address the challenges of data heterogeneity within a federated learning framework. FedSB utilizes label smoothing at the client level to prevent overfitting to domain-specifi... | {
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} |
2412.11409 | Multi-modal and Multi-scale Spatial Environment Understanding for
Immersive Visual Text-to-Speech | [
"cs.CV",
"cs.AI",
"cs.MM"
] | Visual Text-to-Speech (VTTS) aims to take the environmental image as the prompt to synthesize the reverberant speech for the spoken content. The challenge of this task lies in understanding the spatial environment from the image. Many attempts have been made to extract global spatial visual information from the RGB spa... | {
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} |
2412.11410 | MGDA: Model-based Goal Data Augmentation for Offline Goal-conditioned
Weighted Supervised Learning | [
"cs.LG"
] | Recently, a state-of-the-art family of algorithms, known as Goal-Conditioned Weighted Supervised Learning (GCWSL) methods, has been introduced to tackle challenges in offline goal-conditioned reinforcement learning (RL). GCWSL optimizes a lower bound of the goal-conditioned RL objective and has demonstrated outstanding... | {
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} |
2412.11412 | V-MIND: Building Versatile Monocular Indoor 3D Detector with Diverse 2D
Annotations | [
"cs.CV"
] | The field of indoor monocular 3D object detection is gaining significant attention, fueled by the increasing demand in VR/AR and robotic applications. However, its advancement is impeded by the limited availability and diversity of 3D training data, owing to the labor-intensive nature of 3D data collection and annotati... | {
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} |
2412.11414 | Biased or Flawed? Mitigating Stereotypes in Generative Language Models
by Addressing Task-Specific Flaws | [
"cs.CL",
"cs.LG"
] | Recent studies have shown that generative language models often reflect and amplify societal biases in their outputs. However, these studies frequently conflate observed biases with other task-specific shortcomings, such as comprehension failure. For example, when a model misinterprets a text and produces a response th... | {
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} |
2412.11417 | RL-LLM-DT: An Automatic Decision Tree Generation Method Based on RL
Evaluation and LLM Enhancement | [
"cs.AI",
"cs.LG"
] | Traditionally, AI development for two-player zero-sum games has relied on two primary techniques: decision trees and reinforcement learning (RL). A common approach involves using a fixed decision tree as one player's strategy while training an RL agent as the opponent to identify vulnerabilities in the decision tree, t... | {
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} |
2412.11418 | ConceptEdit: Conceptualization-Augmented Knowledge Editing in Large
Language Models for Commonsense Reasoning | [
"cs.CL"
] | Knowledge Editing (KE) aims to adjust a Large Language Model's (LLM) internal representations and parameters to correct inaccuracies and improve output consistency without incurring the computational expense of re-training the entire model. However, editing commonsense knowledge still faces difficulties, including limi... | {
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} |
2412.11420 | Category Level 6D Object Pose Estimation from a Single RGB Image using
Diffusion | [
"cs.CV"
] | Estimating the 6D pose and 3D size of an object from an image is a fundamental task in computer vision. Most current approaches are restricted to specific instances with known models or require ground truth depth information or point cloud captures from LIDAR. We tackle the harder problem of pose estimation for categor... | {
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} |
2412.11422 | Quantum search in a dictionary based on fingerprinting-hashing | [
"quant-ph",
"cs.DS",
"cs.IT",
"math.IT"
] | In this work, we present a quantum query algorithm for searching a word of length $m$ in an unsorted dictionary of size $n$. The algorithm uses $O(\sqrt{n})$ queries (Grover operators), like previously known algorithms. What is new is that the algorithm is based on the quantum fingerprinting-hashing technique, which ... | {
"Other": 1,
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} |
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