id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2411.15215 | S$^2$ALM: Sequence-Structure Pre-trained Large Language Model for
Comprehensive Antibody Representation Learning | [
"cs.LG",
"cs.AI",
"q-bio.BM"
] | Antibodies safeguard our health through their precise and potent binding to specific antigens, demonstrating promising therapeutic efficacy in the treatment of numerous diseases, including COVID-19. Recent advancements in biomedical language models have shown the great potential to interpret complex biological structur... | {
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2411.15216 | Dist Loss: Enhancing Regression in Few-Shot Region through Distribution
Distance Constraint | [
"cs.LG",
"cs.AI"
] | Imbalanced data distributions are prevalent in real-world scenarios, posing significant challenges in both imbalanced classification and imbalanced regression tasks. They often cause deep learning models to overfit in areas of high sample density (many-shot regions) while underperforming in areas of low sample density ... | {
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2411.15217 | LPLgrad: Optimizing Active Learning Through Gradient Norm Sample
Selection and Auxiliary Model Training | [
"cs.LG",
"cs.AI",
"cs.CV"
] | Machine learning models are increasingly being utilized across various fields and tasks due to their outstanding performance and strong generalization capabilities. Nonetheless, their success hinges on the availability of large volumes of annotated data, the creation of which is often labor-intensive, time-consuming, a... | {
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2411.15218 | Suspected Undeclared Use of Artificial Intelligence in the Academic
Literature: An Analysis of the Academ-AI Dataset | [
"cs.DL",
"cs.AI",
"cs.CY"
] | Since generative artificial intelligence (AI) tools such as OpenAI's ChatGPT became widely available, researchers have used them in the writing process. The consensus of the academic publishing community is that such usage must be declared in the published article. Academ-AI documents examples of suspected undeclared A... | {
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2411.15220 | Sampling with Adaptive Variance for Multimodal Distributions | [
"cs.LG",
"cs.NA",
"math.NA",
"stat.CO",
"stat.ML"
] | We propose and analyze a class of adaptive sampling algorithms for multimodal distributions on a bounded domain, which share a structural resemblance to the classic overdamped Langevin dynamics. We first demonstrate that this class of linear dynamics with adaptive diffusion coefficients and vector fields can be interpr... | {
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2411.15221 | Reflections from the 2024 Large Language Model (LLM) Hackathon for
Applications in Materials Science and Chemistry | [
"cs.LG",
"cond-mat.mtrl-sci",
"physics.chem-ph"
] | Here, we present the outcomes from the second Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry, which engaged participants across global hybrid locations, resulting in 34 team submissions. The submissions spanned seven key application areas and demonstrated the diverse utility of... | {
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2411.15222 | Rethinking the Intermediate Features in Adversarial Attacks: Misleading
Robotic Models via Adversarial Distillation | [
"cs.LG",
"cs.AI",
"cs.RO"
] | Language-conditioned robotic learning has significantly enhanced robot adaptability by enabling a single model to execute diverse tasks in response to verbal commands. Despite these advancements, security vulnerabilities within this domain remain largely unexplored. This paper addresses this gap by proposing a novel ad... | {
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2411.15223 | An accuracy improving method for advertising click through rate
prediction based on enhanced xDeepFM model | [
"cs.LG"
] | Advertising click-through rate (CTR) prediction aims to forecast the probability that a user will click on an advertisement in a given context, thus providing enterprises with decision support for product ranking and ad placement. However, CTR prediction faces challenges such as data sparsity and class imbalance, which... | {
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2411.15224 | Parameter Efficient Mamba Tuning via Projector-targeted Diagonal-centric
Linear Transformation | [
"cs.LG",
"cs.AI"
] | Despite the growing interest in Mamba architecture as a potential replacement for Transformer architecture, parameter-efficient fine-tuning (PEFT) approaches for Mamba remain largely unexplored. In our study, we introduce two key insights-driven strategies for PEFT in Mamba architecture: (1) While state-space models (S... | {
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2411.15229 | Learning-Enabled Adaptive Voltage Protection Against Load Alteration
Attacks On Smart Grids | [
"eess.SY",
"cs.CR",
"cs.GT",
"cs.SY"
] | Smart grids are designed to efficiently handle variable power demands, especially for large loads, by real-time monitoring, distributed generation and distribution of electricity. However, the grid's distributed nature and the internet connectivity of large loads like Heating Ventilation, and Air Conditioning (HVAC) sy... | {
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2411.15230 | A No Free Lunch Theorem for Human-AI Collaboration | [
"cs.AI",
"cs.HC",
"cs.LG"
] | The gold standard in human-AI collaboration is complementarity -- when combined performance exceeds both the human and algorithm alone. We investigate this challenge in binary classification settings where the goal is to maximize 0-1 accuracy. Given two or more agents who can make calibrated probabilistic predictions, ... | {
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2411.15231 | IterIS: Iterative Inference-Solving Alignment for LoRA Merging | [
"cs.LG",
"cs.AI"
] | Low-rank adaptations (LoRA) are widely used to fine-tune large models across various domains for specific downstream tasks. While task-specific LoRAs are often available, concerns about data privacy and intellectual property can restrict access to training data, limiting the acquisition of a multi-task model through gr... | {
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2411.15232 | BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models | [
"cs.CV",
"cs.CL"
] | Recent advancements in vision-language models (VLMs), such as CLIP, have demonstrated substantial success in self-supervised representation learning for vision tasks. However, effectively adapting VLMs to downstream applications remains challenging, as their accuracy often depends on time-intensive and expertise-demand... | {
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2411.15233 | Learning Volumetric Neural Deformable Models to Recover 3D Regional
Heart Wall Motion from Multi-Planar Tagged MRI | [
"eess.IV",
"cs.CV"
] | Multi-planar tagged MRI is the gold standard for regional heart wall motion evaluation. However, accurate recovery of the 3D true heart wall motion from a set of 2D apparent motion cues is challenging, due to incomplete sampling of the true motion and difficulty in information fusion from apparent motion cues observed ... | {
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2411.15234 | Adaptive Intelligence: leveraging insights from adaptive behavior in
animals to build flexible AI systems | [
"q-bio.NC",
"cs.AI"
] | Biological intelligence is inherently adaptive -- animals continually adjust their actions based on environmental feedback. However, creating adaptive artificial intelligence (AI) remains a major challenge. The next frontier is to go beyond traditional AI to develop "adaptive intelligence," defined here as harnessing i... | {
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2411.15235 | CODE-CL: COnceptor-Based Gradient Projection for DEep Continual Learning | [
"cs.LG",
"cs.AI",
"cs.CV",
"cs.NE"
] | Continual learning, or the ability to progressively integrate new concepts, is fundamental to intelligent beings, enabling adaptability in dynamic environments. In contrast, artificial deep neural networks face the challenge of catastrophic forgetting when learning new tasks sequentially. To alleviate the problem of fo... | {
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2411.15236 | Text Embedding is Not All You Need: Attention Control for Text-to-Image
Semantic Alignment with Text Self-Attention Maps | [
"cs.CV",
"cs.LG"
] | In text-to-image diffusion models, the cross-attention map of each text token indicates the specific image regions attended. Comparing these maps of syntactically related tokens provides insights into how well the generated image reflects the text prompt. For example, in the prompt, "a black car and a white clock", the... | {
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2411.15237 | Stain-Invariant Representation for Tissue Classification in Histology
Images | [
"cs.CV",
"cs.AI",
"cs.LG"
] | The process of digitising histology slides involves multiple factors that can affect a whole slide image's (WSI) final appearance, including the staining protocol, scanner, and tissue type. This variability constitutes a domain shift and results in significant problems when training and testing deep learning (DL) algor... | {
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2411.15238 | Analysis of the impact of heterogeneous platoon for mixed traffic flow:
control strategy, fuel consumption and emissions | [
"eess.SY",
"cs.SY"
] | Compared with traditional vehicle longitudinal spacing control strategies, the combination spacing strategy can integrate the advantages of different spacing control strategies. However, the impact mechanism of different combination spacing control strategies on mixed traffic flow has not been analyzed yet. Therefore, ... | {
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2411.15239 | Faithful Label-free Knowledge Distillation | [
"cs.CV"
] | Knowledge distillation approaches are model compression techniques, with the goal of training a highly performant student model by using a teacher network that is larger or contains a different inductive bias. These approaches are particularly useful when applied to large computer vision foundation models, which can be... | {
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2411.15240 | AI Foundation Models for Wearable Movement Data in Mental Health
Research | [
"cs.LG",
"cs.AI",
"cs.HC",
"q-bio.QM"
] | Pretrained foundation models and transformer architectures have driven the success of large language models (LLMs) and other modern AI breakthroughs. However, similar advancements in health data modeling remain limited due to the need for innovative adaptations. Wearable movement data offers a valuable avenue for explo... | {
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2411.15241 | EfficientViM: Efficient Vision Mamba with Hidden State Mixer based State
Space Duality | [
"cs.CV"
] | For the deployment of neural networks in resource-constrained environments, prior works have built lightweight architectures with convolution and attention for capturing local and global dependencies, respectively. Recently, the state space model has emerged as an effective global token interaction with its favorable l... | {
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2411.15242 | The Zamba2 Suite: Technical Report | [
"cs.LG",
"cs.AI",
"cs.CL"
] | In this technical report, we present the Zamba2 series -- a suite of 1.2B, 2.7B, and 7.4B parameter hybrid Mamba2-transformer models that achieve state of the art performance against the leading open-weights models of their class, while achieving substantial gains in inference latency, throughput, and memory efficiency... | {
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2411.15243 | Bio-inspired AI: Integrating Biological Complexity into Artificial
Intelligence | [
"q-bio.NC",
"cs.AI",
"cs.CL",
"cs.CV",
"cs.NE",
"cs.SC"
] | The pursuit of creating artificial intelligence (AI) mirrors our longstanding fascination with understanding our own intelligence. From the myths of Talos to Aristotelian logic and Heron's inventions, we have sought to replicate the marvels of the mind. While recent advances in AI hold promise, singular approaches ofte... | {
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2411.15244 | Adversarial Prompt Distillation for Vision-Language Models | [
"cs.CV",
"cs.AI"
] | Large pre-trained Vision-Language Models (VLMs) such as Contrastive Language-Image Pre-Training (CLIP) have been shown to be susceptible to adversarial attacks, raising concerns about their deployment in safety-critical scenarios like autonomous driving and medical diagnosis. One promising approach for improving the ro... | {
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2411.15245 | AnyText2: Visual Text Generation and Editing With Customizable
Attributes | [
"cs.CV"
] | As the text-to-image (T2I) domain progresses, generating text that seamlessly integrates with visual content has garnered significant attention. However, even with accurate text generation, the inability to control font and color can greatly limit certain applications, and this issue remains insufficiently addressed. T... | {
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2411.15247 | Reward Fine-Tuning Two-Step Diffusion Models via Learning Differentiable
Latent-Space Surrogate Reward | [
"cs.LG"
] | Recent research has shown that fine-tuning diffusion models (DMs) with arbitrary rewards, including non-differentiable ones, is feasible with reinforcement learning (RL) techniques, enabling flexible model alignment. However, applying existing RL methods to timestep-distilled DMs is challenging for ultra-fast ($\le2$-s... | {
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2411.15248 | J-Invariant Volume Shuffle for Self-Supervised Cryo-Electron Tomogram
Denoising on Single Noisy Volume | [
"eess.IV",
"cs.CV"
] | Cryo-Electron Tomography (Cryo-ET) enables detailed 3D visualization of cellular structures in near-native states but suffers from low signal-to-noise ratio due to imaging constraints. Traditional denoising methods and supervised learning approaches often struggle with complex noise patterns and the lack of paired data... | {
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2411.15250 | TPLogAD: Unsupervised Log Anomaly Detection Based on Event Templates and
Key Parameters | [
"cs.LG",
"cs.AI",
"cs.CL",
"cs.CY"
] | Log-system is an important mechanism for recording the runtime status and events of Web service systems, and anomaly detection in logs is an effective method of detecting problems. However, manual anomaly detection in logs is inefficient, error-prone, and unrealistic. Existing log anomaly detection methods either use t... | {
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2411.15251 | Optimized Vessel Segmentation: A Structure-Agnostic Approach with Small
Vessel Enhancement and Morphological Correction | [
"eess.IV",
"cs.AI",
"cs.CV"
] | Accurate segmentation of blood vessels is essential for various clinical assessments and postoperative analyses. However, the inherent challenges of vascular imaging, such as sparsity, fine granularity, low contrast, data distribution variability, and the critical need for preserving topological structure, making gener... | {
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2411.15252 | LocRef-Diffusion:Tuning-Free Layout and Appearance-Guided Generation | [
"cs.CV",
"cs.AI"
] | Recently, text-to-image models based on diffusion have achieved remarkable success in generating high-quality images. However, the challenge of personalized, controllable generation of instances within these images remains an area in need of further development. In this paper, we present LocRef-Diffusion, a novel, tuni... | {
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2411.15253 | Unsupervised Machine Learning for Osteoporosis Diagnosis Using Singh
Index Clustering on Hip Radiographs | [
"eess.IV",
"cs.CV",
"cs.LG"
] | Osteoporosis, a prevalent condition among the aging population worldwide, is characterized by diminished bone mass and altered bone structure, increasing susceptibility to fractures. It poses a significant and growing global public health challenge over the next decade. Diagnosis typically involves Dual-energy X-ray ab... | {
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2411.15254 | A Unified Energy Management Framework for Multi-Timescale Forecasting in
Smart Grids | [
"cs.LG",
"cs.AI"
] | Accurate forecasting of the electrical load, such as the magnitude and the timing of peak power, is crucial to successful power system management and implementation of smart grid strategies like demand response and peak shaving. In multi-time-scale optimization scheduling, rolling optimization is a common solution. How... | {
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2411.15255 | OSMamba: Omnidirectional Spectral Mamba with Dual-Domain Prior Generator
for Exposure Correction | [
"eess.IV",
"cs.CV",
"cs.LG"
] | Exposure correction is a fundamental problem in computer vision and image processing. Recently, frequency domain-based methods have achieved impressive improvement, yet they still struggle with complex real-world scenarios under extreme exposure conditions. This is due to the local convolutional receptive fields failin... | {
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2411.15257 | The Explabox: Model-Agnostic Machine Learning Transparency & Analysis | [
"cs.LG",
"cs.AI",
"cs.SE"
] | We present the Explabox: an open-source toolkit for transparent and responsible machine learning (ML) model development and usage. Explabox aids in achieving explainable, fair and robust models by employing a four-step strategy: explore, examine, explain and expose. These steps offer model-agnostic analyses that transf... | {
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2411.15260 | VIVID-10M: A Dataset and Baseline for Versatile and Interactive Video
Local Editing | [
"cs.CV",
"cs.AI"
] | Diffusion-based image editing models have made remarkable progress in recent years. However, achieving high-quality video editing remains a significant challenge. One major hurdle is the absence of open-source, large-scale video editing datasets based on real-world data, as constructing such datasets is both time-consu... | {
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2411.15262 | MovieBench: A Hierarchical Movie Level Dataset for Long Video Generation | [
"cs.CV"
] | Recent advancements in video generation models, like Stable Video Diffusion, show promising results, but primarily focus on short, single-scene videos. These models struggle with generating long videos that involve multiple scenes, coherent narratives, and consistent characters. Furthermore, there is no publicly availa... | {
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2411.15263 | AI-Driven Real-Time Monitoring of Ground-Nesting Birds: A Case Study on
Curlew Detection Using YOLOv10 | [
"cs.CV",
"cs.AI"
] | Effective monitoring of wildlife is critical for assessing biodiversity and ecosystem health, as declines in key species often signal significant environmental changes. Birds, particularly ground-nesting species, serve as important ecological indicators due to their sensitivity to environmental pressures. Camera traps ... | {
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2411.15265 | Derivative-Free Diffusion Manifold-Constrained Gradient for Unified XAI | [
"cs.CV",
"cs.LG"
] | Gradient-based methods are a prototypical family of explainability techniques, especially for image-based models. Nonetheless, they have several shortcomings in that they (1) require white-box access to models, (2) are vulnerable to adversarial attacks, and (3) produce attributions that lie off the image manifold, lead... | {
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2411.15266 | Continuous Design and Reprogramming of Totimorphic Structures for Space
Applications | [
"astro-ph.IM",
"cond-mat.dis-nn",
"cond-mat.mtrl-sci",
"cs.RO",
"physics.class-ph"
] | Recently, a class of mechanical lattices with reconfigurable, zero-stiffness structures has been proposed, called Totimorphic structures. In this work, we introduce a computational framework that allows continuous reprogramming of a Totimorphic lattice's effective properties, such as mechanical and optical properties, ... | {
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2411.15267 | Proportional infinite-width infinite-depth limit for deep linear neural
networks | [
"stat.ML",
"cond-mat.dis-nn",
"cs.LG",
"math.PR"
] | We study the distributional properties of linear neural networks with random parameters in the context of large networks, where the number of layers diverges in proportion to the number of neurons per layer. Prior works have shown that in the infinite-width regime, where the number of neurons per layer grows to infinit... | {
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2411.15268 | ICT: Image-Object Cross-Level Trusted Intervention for Mitigating Object
Hallucination in Large Vision-Language Models | [
"cs.CV",
"cs.CL"
] | Despite the recent breakthroughs achieved by Large Vision Language Models (LVLMs) in understanding and responding to complex visual-textual contexts, their inherent hallucination tendencies limit their practical application in real-world scenarios that demand high levels of precision. Existing methods typically either ... | {
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2411.15269 | MambaIRv2: Attentive State Space Restoration | [
"eess.IV",
"cs.CV",
"cs.LG"
] | The Mamba-based image restoration backbones have recently demonstrated significant potential in balancing global reception and computational efficiency. However, the inherent causal modeling limitation of Mamba, where each token depends solely on its predecessors in the scanned sequence, restricts the full utilization ... | {
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2411.15270 | BanglaEmbed: Efficient Sentence Embedding Models for a Low-Resource
Language Using Cross-Lingual Distillation Techniques | [
"cs.CL",
"cs.LG"
] | Sentence-level embedding is essential for various tasks that require understanding natural language. Many studies have explored such embeddings for high-resource languages like English. However, low-resource languages like Bengali (a language spoken by almost two hundred and thirty million people) are still under-explo... | {
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2411.15271 | EADReg: Probabilistic Correspondence Generation with Efficient
Autoregressive Diffusion Model for Outdoor Point Cloud Registration | [
"cs.CV",
"cs.AI"
] | Diffusion models have shown the great potential in the point cloud registration (PCR) task, especially for enhancing the robustness to challenging cases. However, existing diffusion-based PCR methods primarily focus on instance-level scenarios and struggle with outdoor LiDAR points, where the sparsity, irregularity, an... | {
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2411.15272 | Curriculum-enhanced GroupDRO: Challenging the Norm of Avoiding
Curriculum Learning in Subpopulation Shift Setups | [
"cs.LG",
"cs.AI"
] | In subpopulation shift scenarios, a Curriculum Learning (CL) approach would only serve to imprint the model weights, early on, with the easily learnable spurious correlations featured. To the best of our knowledge, none of the current state-of-the-art subpopulation shift approaches employ any kind of curriculum. To ove... | {
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2411.15274 | Feature-interactive Siamese graph encoder-based image analysis to
predict STAS from histopathology images in lung cancer | [
"eess.IV",
"cs.AI",
"cs.CV"
] | Spread through air spaces (STAS) is a distinct invasion pattern in lung cancer, crucial for prognosis assessment and guiding surgical decisions. Histopathology is the gold standard for STAS detection, yet traditional methods are subjective, time-consuming, and prone to misdiagnosis, limiting large-scale applications. W... | {
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2411.15276 | Event USKT : U-State Space Model in Knowledge Transfer for Event Cameras | [
"cs.CV",
"cs.AI"
] | Event cameras, as an emerging imaging technology, offer distinct advantages over traditional RGB cameras, including reduced energy consumption and higher frame rates. However, the limited quantity of available event data presents a significant challenge, hindering their broader development. To alleviate this issue, we ... | {
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2411.15277 | Foundation Cures Personalization: Recovering Facial Personalized Models'
Prompt Consistency | [
"cs.CV"
] | Facial personalization represents a crucial downstream task in the domain of text-to-image generation. To preserve identity fidelity while ensuring alignment with user-defined prompts, current mainstream frameworks for facial personalization predominantly employ identity embedding mechanisms to associate identity infor... | {
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2411.15279 | Don't Mesh with Me: Generating Constructive Solid Geometry Instead of
Meshes by Fine-Tuning a Code-Generation LLM | [
"cs.LG",
"cs.GR"
] | While recent advancements in machine learning, such as LLMs, are revolutionizing software development and creative industries, they have had minimal impact on engineers designing mechanical parts, which remains largely a manual process. Existing approaches to generate 3D geometry most commonly use meshes as a 3D repres... | {
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2411.15281 | ElastiFormer: Learned Redundancy Reduction in Transformer via
Self-Distillation | [
"cs.LG",
"cs.AI"
] | We introduce ElastiFormer, a post-training technique that adapts pretrained Transformer models into an elastic counterpart with variable inference time compute. ElastiFormer introduces small routing modules (as low as .00006% additional trainable parameters) to dynamically selects subsets of network parameters and inpu... | {
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2411.15283 | A Plug-and-Play Temporal Normalization Module for Robust Remote
Photoplethysmography | [
"eess.IV",
"cs.CV"
] | Remote photoplethysmography (rPPG) extracts PPG signals from subtle color changes in facial videos, showing strong potential for health applications. However, most rPPG methods rely on intensity differences between consecutive frames, missing long-term signal variations affected by motion or lighting artifacts, which r... | {
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2411.15284 | When Spatial meets Temporal in Action Recognition | [
"cs.CV",
"cs.LG"
] | Video action recognition has made significant strides, but challenges remain in effectively using both spatial and temporal information. While existing methods often focus on either spatial features (e.g., object appearance) or temporal dynamics (e.g., motion), they rarely address the need for a comprehensive integrati... | {
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2411.15285 | Forecasting Unseen Points of Interest Visits Using Context and Proximity
Priors | [
"cs.LG",
"cs.AI"
] | Understanding human mobility behavior is crucial for numerous applications, including crowd management, location-based recommendations, and the estimation of pandemic spread. Machine learning models can predict the Points of Interest (POIs) that individuals are likely to visit in the future by analyzing their historica... | {
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2411.15287 | Sycophancy in Large Language Models: Causes and Mitigations | [
"cs.CL",
"cs.AI"
] | Large language models (LLMs) have demonstrated remarkable capabilities across a wide range of natural language processing tasks. However, their tendency to exhibit sycophantic behavior - excessively agreeing with or flattering users - poses significant risks to their reliability and ethical deployment. This paper provi... | {
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2411.15288 | There is no SAMantics! Exploring SAM as a Backbone for Visual
Understanding Tasks | [
"cs.CV"
] | The Segment Anything Model (SAM) was originally designed for label-agnostic mask generation. Does this model also possess inherent semantic understanding, of value to broader visual tasks? In this work we follow a multi-staged approach towards exploring this question. We firstly quantify SAM's semantic capabilities by ... | {
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2411.15290 | GreenMachine: Automatic Design of Zero-Cost Proxies for Energy-Efficient
NAS | [
"cs.LG",
"cs.NE"
] | Artificial Intelligence (AI) has driven innovations and created new opportunities across various sectors. However, leveraging domain-specific knowledge often requires automated tools to design and configure models effectively. In the case of Deep Neural Networks (DNNs), researchers and practitioners usually resort to N... | {
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2411.15292 | Influence functions and regularity tangents for efficient active
learning | [
"cs.LG",
"cs.AI",
"math.ST",
"stat.ML",
"stat.TH"
] | In this paper we describe an efficient method for providing a regression model with a sense of curiosity about its data. In the field of machine learning, our framework for representing curiosity is called Active Learning, which means automatically choosing data points for which to query labels in the semisupervised se... | {
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2411.15295 | Frequency-Guided Posterior Sampling for Diffusion-Based Image
Restoration | [
"eess.IV",
"cs.CV",
"cs.LG",
"stat.ML"
] | Image restoration aims to recover high-quality images from degraded observations. When the degradation process is known, the recovery problem can be formulated as an inverse problem, and in a Bayesian context, the goal is to sample a clean reconstruction given the degraded observation. Recently, modern pretrained diffu... | {
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2411.15296 | MME-Survey: A Comprehensive Survey on Evaluation of Multimodal LLMs | [
"cs.CV",
"cs.AI",
"cs.CL"
] | As a prominent direction of Artificial General Intelligence (AGI), Multimodal Large Language Models (MLLMs) have garnered increased attention from both industry and academia. Building upon pre-trained LLMs, this family of models further develops multimodal perception and reasoning capabilities that are impressive, such... | {
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2411.15306 | Heavy-tailed Contamination is Easier than Adversarial Contamination | [
"math.ST",
"cs.DS",
"cs.LG",
"stat.ME",
"stat.ML",
"stat.TH"
] | A large body of work in the statistics and computer science communities dating back to Huber (Huber, 1960) has led to statistically and computationally efficient outlier-robust estimators. Two particular outlier models have received significant attention: the adversarial and heavy-tailed models. While the former models... | {
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2411.15315 | Lie-Equivariant Quantum Graph Neural Networks | [
"quant-ph",
"cs.LG",
"hep-ex",
"hep-ph"
] | Discovering new phenomena at the Large Hadron Collider (LHC) involves the identification of rare signals over conventional backgrounds. Thus binary classification tasks are ubiquitous in analyses of the vast amounts of LHC data. We develop a Lie-Equivariant Quantum Graph Neural Network (Lie-EQGNN), a quantum model that... | {
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2411.15318 | Direct And Inverse Dynamics Problems For A Three-wheel Mobile Robot With
Two Drive Wheels | [
"cs.RO",
"math.OC"
] | Mobile robots are widely used to perform various technological operations in several sectors of the national economy. These operations are related to transporting goods and equipment, performing work to determine the condition of a technical object or structure, their construction or repair, performing work to study a ... | {
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2411.15319 | Scalable and Optimal Security Allocation in Networks against Stealthy
Injection Attacks | [
"eess.SY",
"cs.SY"
] | This paper addresses the security allocation problem in a networked control system under stealthy injection attacks. The networked system is comprised of interconnected subsystems which are represented by nodes in a digraph. An adversary compromises the system by injecting false data into several nodes with the aim of ... | {
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2411.15320 | PPLqa: An Unsupervised Information-Theoretic Quality Metric for
Comparing Generative Large Language Models | [
"cs.CL",
"cs.AI"
] | We propose PPLqa, an easy to compute, language independent, information-theoretic metric to measure the quality of responses of generative Large Language Models (LLMs) in an unsupervised way, without requiring ground truth annotations or human supervision. The method and metric enables users to rank generative language... | {
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2411.15322 | Deep Learning-Based Automatic Delineation of Liver Domes in kV Triggered
Images for Online Breath-hold Reproducibility Verification of Liver
Stereotactic Body Radiation Therapy | [
"physics.med-ph",
"cs.CV"
] | Stereotactic Body Radiation Therapy (SBRT) can be a precise, minimally invasive treatment method for liver cancer and liver metastases. However, the effectiveness of SBRT relies on the accurate delivery of the dose to the tumor while sparing healthy tissue. Challenges persist in ensuring breath-hold reproducibility, wi... | {
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2411.15328 | Dependence Induced Representations | [
"cs.LG",
"stat.ML"
] | We study the problem of learning feature representations from a pair of random variables, where we focus on the representations that are induced by their dependence. We provide sufficient and necessary conditions for such dependence induced representations, and illustrate their connections to Hirschfeld--Gebelein--R\'{... | {
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2411.15331 | GeoScatt-GNN: A Geometric Scattering Transform-Based Graph Neural
Network Model for Ames Mutagenicity Prediction | [
"cs.LG",
"cs.AI",
"eess.IV",
"q-bio.QM"
] | This paper tackles the pressing challenge of mutagenicity prediction by introducing three ground-breaking approaches. First, it showcases the superior performance of 2D scattering coefficients extracted from molecular images, compared to traditional molecular descriptors. Second, it presents a hybrid approach that comb... | {
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2411.15349 | Zero-Shot Coreset Selection: Efficient Pruning for Unlabeled Data | [
"cs.CV"
] | Deep learning increasingly relies on massive data with substantial costs for storage, annotation, and model training. To reduce these costs, coreset selection aims to find a representative subset of data to train models while ideally performing on par with the full data training. State-of-the-art coreset methods use ca... | {
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2411.15350 | Dynamic Tube MPC: Learning Tube Dynamics with Massively Parallel
Simulation for Robust Safety in Practice | [
"cs.RO",
"cs.LG",
"cs.SY",
"eess.SY"
] | Safe navigation of cluttered environments is a critical challenge in robotics. It is typically approached by separating the planning and tracking problems, with planning executed on a reduced order model to generate reference trajectories, and control techniques used to track these trajectories on the full order dynami... | {
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2411.15351 | Accelerating CALPHAD-based Phase Diagram Predictions in Complex Alloys
Using Universal Machine Learning Potentials: Opportunities and Challenges | [
"cond-mat.mtrl-sci",
"cs.LG"
] | Accurate phase diagram prediction is crucial for understanding alloy thermodynamics and advancing materials design. While traditional CALPHAD methods are robust, they are resource-intensive and limited by experimentally assessed data. This work explores the use of machine learning interatomic potentials (MLIPs) such as... | {
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2411.15355 | UniGaussian: Driving Scene Reconstruction from Multiple Camera Models
via Unified Gaussian Representations | [
"cs.CV",
"cs.AI"
] | Urban scene reconstruction is crucial for real-world autonomous driving simulators. Although existing methods have achieved photorealistic reconstruction, they mostly focus on pinhole cameras and neglect fisheye cameras. In fact, how to effectively simulate fisheye cameras in driving scene remains an unsolved problem. ... | {
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2411.15356 | Regulator-Manufacturer AI Agents Modeling: Mathematical Feedback-Driven
Multi-Agent LLM Framework | [
"cs.AI",
"cs.MA"
] | The increasing complexity of regulatory updates from global authorities presents significant challenges for medical device manufacturers, necessitating agile strategies to sustain compliance and maintain market access. Concurrently, regulatory bodies must effectively monitor manufacturers' responses and develop strateg... | {
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2411.15361 | Designing Cellular Manufacturing System in Presence of Alternative
Process Plans | [
"cs.AI"
] | In the design of cellular manufacturing systems (CMS), numerous technological and managerial decisions must be made at both the design and operational stages. The first step in designing a CMS involves grouping parts and machines. In this paper, four integer programming formulations are presented for grouping parts and... | {
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2411.15364 | Exploring Facets of Language Generation in the Limit | [
"cs.DS",
"cs.AI",
"cs.CL",
"cs.LG"
] | The recent work of Kleinberg & Mullainathan [KM24] provides a concrete model for language generation in the limit: given a sequence of examples from an unknown target language, the goal is to generate new examples from the target language such that no incorrect examples are generated beyond some point. In sharp contras... | {
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2411.15366 | Personalization of Wearable Sensor-Based Joint Kinematic Estimation
Using Computer Vision for Hip Exoskeleton Applications | [
"cs.RO",
"cs.CV"
] | Accurate lower-limb joint kinematic estimation is critical for applications such as patient monitoring, rehabilitation, and exoskeleton control. While previous studies have employed wearable sensor-based deep learning (DL) models for estimating joint kinematics, these methods often require extensive new datasets to ada... | {
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2411.15367 | Exploiting Watermark-Based Defense Mechanisms in Text-to-Image Diffusion
Models for Unauthorized Data Usage | [
"cs.CV",
"cs.AI"
] | Text-to-image diffusion models, such as Stable Diffusion, have shown exceptional potential in generating high-quality images. However, recent studies highlight concerns over the use of unauthorized data in training these models, which may lead to intellectual property infringement or privacy violations. A promising app... | {
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2411.15368 | The Power of Types: Exploring the Impact of Type Checking on Neural Bug
Detection in Dynamically Typed Languages | [
"cs.SE",
"cs.LG",
"cs.PL"
] | Motivation: Automated bug detection in dynamically typed languages such as Python is essential for maintaining code quality. The lack of mandatory type annotations in such languages can lead to errors that are challenging to identify early with traditional static analysis tools. Recent progress in deep neural networks ... | {
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2411.15370 | Deep Policy Gradient Methods Without Batch Updates, Target Networks, or
Replay Buffers | [
"cs.LG",
"cs.AI",
"cs.RO",
"cs.SY",
"eess.SY"
] | Modern deep policy gradient methods achieve effective performance on simulated robotic tasks, but they all require large replay buffers or expensive batch updates, or both, making them incompatible for real systems with resource-limited computers. We show that these methods fail catastrophically when limited to small r... | {
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2411.15371 | Safe and Trustworthy Robot Pathfinding with BIM, MHA*, and NLP | [
"cs.RO"
] | Construction robots have gained significant traction in recent years in research and development. However, the application of industrial robots has unique challenges. Dynamic environments, domain-specific tasks, and complex localization and mapping are significant obstacles in their development. In construction job sit... | {
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2411.15372 | Transforming NLU with Babylon: A Case Study in Development of Real-time,
Edge-Efficient, Multi-Intent Translation System for Automated Drive-Thru
Ordering | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Real-time conversational AI agents face challenges in performing Natural Language Understanding (NLU) in dynamic, outdoor environments like automated drive-thru systems. These settings require NLU models to handle background noise, diverse accents, and multi-intent queries while operating under strict latency and memor... | {
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2411.15375 | AdamZ: An Enhanced Optimisation Method for Neural Network Training | [
"cs.LG",
"cs.AI",
"cs.NE",
"math.OC",
"stat.ML"
] | AdamZ is an advanced variant of the Adam optimiser, developed to enhance convergence efficiency in neural network training. This optimiser dynamically adjusts the learning rate by incorporating mechanisms to address overshooting and stagnation, that are common challenges in optimisation. Specifically, AdamZ reduces the... | {
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2411.15378 | Improved Background Estimation for Gas Plume Identification in
Hyperspectral Images | [
"eess.IV",
"cs.CV",
"cs.LG"
] | Longwave infrared (LWIR) hyperspectral imaging can be used for many tasks in remote sensing, including detecting and identifying effluent gases by LWIR sensors on airborne platforms. Once a potential plume has been detected, it needs to be identified to determine exactly what gas or gases are present in the plume. Duri... | {
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2411.15380 | Nd-BiMamba2: A Unified Bidirectional Architecture for Multi-Dimensional
Data Processing | [
"cs.LG",
"cs.AI"
] | Deep learning models often require specially designed architectures to process data of different dimensions, such as 1D time series, 2D images, and 3D volumetric data. Existing bidirectional models mainly focus on sequential data, making it difficult to scale effectively to higher dimensions. To address this issue, we ... | {
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2411.15382 | On the Impact of Fine-Tuning on Chain-of-Thought Reasoning | [
"cs.CL"
] | Large language models have emerged as powerful tools for general intelligence, showcasing advanced natural language processing capabilities that find applications across diverse domains. Despite their impressive performance, recent studies have highlighted the potential for significant enhancements in LLMs' task-specif... | {
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2411.15385 | Gradient dynamics for low-rank fine-tuning beyond kernels | [
"cs.LG",
"math.ST",
"stat.ML",
"stat.TH"
] | LoRA has emerged as one of the de facto methods for fine-tuning foundation models with low computational cost and memory footprint. The idea is to only train a low-rank perturbation to the weights of a pre-trained model, given supervised data for a downstream task. Despite its empirical sucess, from a mathematical pers... | {
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2411.15386 | Inducing Human-like Biases in Moral Reasoning Language Models | [
"cs.AI",
"cs.CY",
"cs.LG"
] | In this work, we study the alignment (BrainScore) of large language models (LLMs) fine-tuned for moral reasoning on behavioral data and/or brain data of humans performing the same task. We also explore if fine-tuning several LLMs on the fMRI data of humans performing moral reasoning can improve the BrainScore. We fine-... | {
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2411.15387 | From Jack of All Trades to Master of One: Specializing LLM-based
Autoraters to a Test Set | [
"cs.CL"
] | As LLMs continue to become more powerful and versatile, human evaluation has quickly become intractable at scale and reliance on automatic metrics has become the norm. Recently, it has been shown that LLMs are themselves state-of-the-art evaluators for many tasks. These Autoraters are typically designed so that they ge... | {
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2411.15388 | A Constrast-Agnostic Method for Ultra-High Resolution Claustrum
Segmentation | [
"cs.CV",
"cs.LG",
"eess.IV"
] | The claustrum is a band-like gray matter structure located between putamen and insula whose exact functions are still actively researched. Its sheet-like structure makes it barely visible in in vivo Magnetic Resonance Imaging (MRI) scans at typical resolutions and neuroimaging tools for its study, including methods for... | {
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} |
2411.15390 | The Hatching-Box: A Novel System for Automated Monitoring and
Quantification of Drosophila melanogaster Developmental Behavior | [
"cs.CV"
] | In this paper we propose the Hatching-Box, a novel imaging and analysis system to automatically monitor and quantify the developmental behavior of Drosophila in standard rearing vials and during regular rearing routines, rendering explicit experiments obsolete. This is achieved by combining custom tailored imaging hard... | {
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} |
2411.15393 | Gradient-Free Classifier Guidance for Diffusion Model Sampling | [
"cs.CV",
"cs.AI"
] | Image generation using diffusion models have demonstrated outstanding learning capabilities, effectively capturing the full distribution of the training dataset. They are known to generate wide variations in sampled images, albeit with a trade-off in image fidelity. Guided sampling methods, such as classifier guidance ... | {
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} |
2411.15395 | ChatBCI: A P300 Speller BCI Leveraging Large Language Models for
Improved Sentence Composition in Realistic Scenarios | [
"cs.HC",
"cs.AI",
"cs.CL",
"cs.SY",
"eess.SP",
"eess.SY"
] | P300 speller BCIs allow users to compose sentences by selecting target keys on a GUI through the detection of P300 component in their EEG signals following visual stimuli. Most P300 speller BCIs require users to spell words letter by letter, or the first few initial letters, resulting in high keystroke demands that inc... | {
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} |
2411.15396 | The Decoy Dilemma in Online Medical Information Evaluation: A
Comparative Study of Credibility Assessments by LLM and Human Judges | [
"cs.IR",
"cs.AI",
"cs.HC"
] | Can AI be cognitively biased in automated information judgment tasks? Despite recent progresses in measuring and mitigating social and algorithmic biases in AI and large language models (LLMs), it is not clear to what extent LLMs behave "rationally", or if they are also vulnerable to human cognitive bias triggers. To a... | {
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} |
2411.15397 | Efficient Online Inference of Vision Transformers by Training-Free
Tokenization | [
"cs.CV"
] | The cost of deploying vision transformers increasingly represents a barrier to wider industrial adoption. Existing compression requires additional end-to-end fine-tuning or incurs a significant drawback to runtime, thus making them ill-suited for online inference. We introduce the $\textbf{Visual Word Tokenizer}$ (VWT)... | {
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} |
2411.15399 | Less is More: Optimizing Function Calling for LLM Execution on Edge
Devices | [
"cs.PF",
"cs.DC",
"cs.LG"
] | The advanced function-calling capabilities of foundation models open up new possibilities for deploying agents to perform complex API tasks. However, managing large amounts of data and interacting with numerous APIs makes function calling hardware-intensive and costly, especially on edge devices. Current Large Language... | {
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} |
2411.15403 | Partial Knowledge Distillation for Alleviating the Inherent Inter-Class
Discrepancy in Federated Learning | [
"cs.LG"
] | Substantial efforts have been devoted to alleviating the impact of the long-tailed class distribution in federated learning. In this work, we observe an interesting phenomenon that weak classes consistently exist even for class-balanced learning. These weak classes, different from the minority classes in the previous w... | {
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} |
2411.15404 | A Comparative Analysis of Transformer and LSTM Models for Detecting
Suicidal Ideation on Reddit | [
"cs.LG",
"cs.CL",
"cs.SI"
] | Suicide is a critical global health problem involving more than 700,000 deaths yearly, particularly among young adults. Many people express their suicidal thoughts on social media platforms such as Reddit. This paper evaluates the effectiveness of the deep learning transformer-based models BERT, RoBERTa, DistilBERT, AL... | {
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} |
2411.15405 | ML-SPEAK: A Theory-Guided Machine Learning Method for Studying and
Predicting Conversational Turn-taking Patterns | [
"cs.CL",
"cs.LG"
] | Predicting team dynamics from personality traits remains a fundamental challenge for the psychological sciences and team-based organizations. Understanding how team composition generates team processes can significantly advance team-based research along with providing practical guidelines for team staffing and training... | {
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} |
2411.15408 | Exploring Large Language Models for Multimodal Sentiment Analysis:
Challenges, Benchmarks, and Future Directions | [
"cs.CL",
"cs.AI"
] | Multimodal Aspect-Based Sentiment Analysis (MABSA) aims to extract aspect terms and their corresponding sentiment polarities from multimodal information, including text and images. While traditional supervised learning methods have shown effectiveness in this task, the adaptability of large language models (LLMs) to MA... | {
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} |
2411.15411 | FINECAPTION: Compositional Image Captioning Focusing on Wherever You
Want at Any Granularity | [
"cs.CV"
] | The advent of large Vision-Language Models (VLMs) has significantly advanced multimodal tasks, enabling more sophisticated and accurate reasoning across various applications, including image and video captioning, visual question answering, and cross-modal retrieval. Despite their superior capabilities, VLMs struggle wi... | {
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} |
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