id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2411.11296 | Steering Language Model Refusal with Sparse Autoencoders | [
"cs.LG"
] | Responsible practices for deploying language models include guiding models to recognize and refuse answering prompts that are considered unsafe, while complying with safe prompts. Achieving such behavior typically requires updating model weights, which is costly and inflexible. We explore opportunities to steering mode... | {
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2411.11300 | Accelerating spherical K-means clustering for large-scale sparse
document data | [
"stat.ML",
"cs.LG"
] | This paper presents an accelerated spherical K-means clustering algorithm for large-scale and high-dimensional sparse document data sets. We design an algorithm working in an architecture-friendly manner (AFM), which is a procedure of suppressing performance-degradation factors such as the numbers of instructions, bran... | {
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2411.11302 | Towards Personalized Brain-Computer Interface Application Based on
Endogenous EEG Paradigms | [
"cs.HC",
"cs.AI"
] | In this paper, we propose a conceptual framework for personalized brain-computer interface (BCI) applications, which can offer an enhanced user experience by customizing services to individual preferences and needs, based on endogenous electroencephalography (EEG) paradigms including motor imagery (MI), speech imagery ... | {
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2411.11303 | Recurrent Stochastic Configuration Networks with Incremental Blocks | [
"cs.LG",
"cs.AI"
] | Recurrent stochastic configuration networks (RSCNs) have shown promise in modelling nonlinear dynamic systems with order uncertainty due to their advantages of easy implementation, less human intervention, and strong approximation capability. This paper develops the original RSCNs with block increments, termed block RS... | {
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2411.11304 | Towards Federated Graph Learning in One-shot Communication | [
"cs.LG"
] | Federated Graph Learning (FGL) has emerged as a promising paradigm for breaking data silos among distributed private graphs. In practical scenarios involving heterogeneous distributed graph data, personalized Federated Graph Learning (pFGL) aims to enhance model utility by training personalized models tailored to clien... | {
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2411.11305 | TP-UNet: Temporal Prompt Guided UNet for Medical Image Segmentation | [
"cs.CV",
"cs.AI"
] | The advancement of medical image segmentation techniques has been propelled by the adoption of deep learning techniques, particularly UNet-based approaches, which exploit semantic information to improve the accuracy of segmentations. However, the order of organs in scanned images has been disregarded by current medical... | {
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2411.11306 | Design a New Pulling Gear for the Automated Pant Bottom Hem Sewing
Machine | [
"cs.RO"
] | Automated machinery design for garment manufacturing is essential for improving productivity, consistency, and quality. This paper focuses on the development of new pulling gear for automated pant bottom hem sewing machines. Traditionally, these machines require manual intervention to guide the bottom hem sewing proces... | {
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2411.11310 | Towards Mitigating Sim2Real Gaps: A Formal Quantitative Approach | [
"eess.SY",
"cs.SY"
] | In this paper, we introduce the notion of simulation-gap functions to formally quantify the potential gap between an approximate nominal mathematical model and the high-fidelity simulator representation of a real system. Given a nominal mathematical model alongside a quantified simulation gap, the system can be concept... | {
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2411.11312 | Study of the Performance of CEEMDAN in Underdetermined Speech Separation | [
"cs.SD",
"cs.AI",
"eess.AS"
] | The CEEMDAN algorithm is one of the modern methods used in the analysis of non-stationary signals. This research presents a study of the effectiveness of this method in audio source separation to know the limits of its work. It concluded two conditions related to frequencies and amplitudes of mixed signals to be separa... | {
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2411.11315 | A Review on Machine Unlearning | [
"cs.LG"
] | Recently, an increasing number of laws have governed the useability of users' privacy. For example, Article 17 of the General Data Protection Regulation (GDPR), the right to be forgotten, requires machine learning applications to remove a portion of data from a dataset and retrain it if the user makes such a request. F... | {
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2411.11318 | Syllabus: Portable Curricula for Reinforcement Learning Agents | [
"cs.AI"
] | Curriculum learning has been a quiet yet crucial component of many of the high-profile successes of reinforcement learning. Despite this, none of the major reinforcement learning libraries directly support curriculum learning or include curriculum learning implementations. These methods can improve the capabilities and... | {
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2411.11323 | SayComply: Grounding Field Robotic Tasks in Operational Compliance
through Retrieval-Based Language Models | [
"cs.RO"
] | This paper addresses the problem of task planning for robots that must comply with operational manuals in real-world settings. Task planning under these constraints is essential for enabling autonomous robot operation in domains that require adherence to domain-specific knowledge. Current methods for generating robot g... | {
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2411.11324 | Cuvis.Ai: An Open-Source, Low-Code Software Ecosystem for Hyperspectral
Processing and Classification | [
"cs.LG",
"cs.SE"
] | Machine learning is an important tool for analyzing high-dimension hyperspectral data; however, existing software solutions are either closed-source or inextensible research products. In this paper, we present cuvis.ai, an open-source and low-code software ecosystem for data acquisition, preprocessing, and model traini... | {
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2411.11325 | Lorentz: Learned SKU Recommendation Using Profile Data | [
"cs.DB"
] | Cloud operators have expanded their service offerings, known as Stock Keeping Units (SKUs), to accommodate diverse demands, resulting in increased complexity for customers to select appropriate configurations. In a studied system, only 43% of the resource capacity was correctly chosen. Automated solutions addressing th... | {
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2411.11326 | Intelligent Pooling: Proactive Resource Provisioning in Large-scale
Cloud Service | [
"cs.DB"
] | The proliferation of big data and analytic workloads has driven the need for cloud compute and cluster-based job processing. With Apache Spark, users can process terabytes of data at ease with hundreds of parallel executors. At Microsoft, we aim at providing a fast and succinct interface for users to run Spark applicat... | {
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2411.11327 | Enhancing Decision Transformer with Diffusion-Based Trajectory Branch
Generation | [
"cs.LG"
] | Decision Transformer (DT) can learn effective policy from offline datasets by converting the offline reinforcement learning (RL) into a supervised sequence modeling task, where the trajectory elements are generated auto-regressively conditioned on the return-to-go (RTG).However, the sequence modeling learning approach ... | {
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2411.11329 | Color-Oriented Redundancy Reduction in Dataset Distillation | [
"cs.CV"
] | Dataset Distillation (DD) is designed to generate condensed representations of extensive image datasets, enhancing training efficiency. Despite recent advances, there remains considerable potential for improvement, particularly in addressing the notable redundancy within the color space of distilled images. In this pap... | {
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2411.11335 | Video-to-Task Learning via Motion-Guided Attention for Few-Shot Action
Recognition | [
"cs.CV"
] | In recent years, few-shot action recognition has achieved remarkable performance through spatio-temporal relation modeling. Although a wide range of spatial and temporal alignment modules have been proposed, they primarily address spatial or temporal misalignments at the video level, while the spatio-temporal relations... | {
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2411.11340 | A Hybrid Loss Framework for Decomposition-based Time Series Forecasting
Methods: Balancing Global and Component Errors | [
"cs.LG",
"stat.ML"
] | Accurate time series forecasting, predicting future values based on past data, is crucial for diverse industries. Many current time series methods decompose time series into multiple sub-series, applying different model architectures and training with an end-to-end overall loss for forecasting. However, this raises a q... | {
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2411.11343 | Teaching Video Diffusion Model with Latent Physical Phenomenon Knowledge | [
"cs.CV",
"stat.AP"
] | Video diffusion models have exhibited tremendous progress in various video generation tasks. However, existing models struggle to capture latent physical knowledge, failing to infer physical phenomena that are challenging to articulate with natural language. Generating videos following the fundamental physical laws is ... | {
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2411.11344 | Mitigating Knowledge Conflicts in Language Model-Driven Question
Answering | [
"cs.CL",
"cs.AI"
] | In the context of knowledge-driven seq-to-seq generation tasks, such as document-based question answering and document summarization systems, two fundamental knowledge sources play crucial roles: the inherent knowledge embedded within model parameters and the external knowledge obtained through context. Recent studies ... | {
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2411.11348 | Modeling Multivariable High-resolution 3D Urban Microclimate Using
Localized Fourier Neural Operator | [
"physics.flu-dyn",
"cs.LG"
] | Accurate urban microclimate analysis with wind velocity and temperature is vital for energy-efficient urban planning, supporting carbon reduction, enhancing public health and comfort, and advancing the low-altitude economy. However, traditional computational fluid dynamics (CFD) simulations that couple velocity and tem... | {
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2411.11350 | Zero-Shot Load Forecasting with Large Language Models | [
"cs.LG",
"eess.SP"
] | Deep learning models have shown strong performance in load forecasting, but they generally require large amounts of data for model training before being applied to new scenarios, which limits their effectiveness in data-scarce scenarios. Inspired by the great success of pre-trained language models (LLMs) in natural lan... | {
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2411.11351 | Visual-Semantic Graph Matching Net for Zero-Shot Learning | [
"cs.CV"
] | Zero-shot learning (ZSL) aims to leverage additional semantic information to recognize unseen classes. To transfer knowledge from seen to unseen classes, most ZSL methods often learn a shared embedding space by simply aligning visual embeddings with semantic prototypes. However, methods trained under this paradigm ofte... | {
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2411.11354 | A comprehensive survey of oracle character recognition: challenges,
benchmarks, and beyond | [
"cs.CV",
"cs.AI"
] | Oracle character recognition-an analysis of ancient Chinese inscriptions found on oracle bones-has become a pivotal field intersecting archaeology, paleography, and historical cultural studies. Traditional methods of oracle character recognition have relied heavily on manual interpretation by experts, which is not only... | {
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2411.11356 | Superpixel-informed Implicit Neural Representation for Multi-Dimensional
Data | [
"cs.CV"
] | Recently, implicit neural representations (INRs) have attracted increasing attention for multi-dimensional data recovery. However, INRs simply map coordinates via a multi-layer perception (MLP) to corresponding values, ignoring the inherent semantic information of the data. To leverage semantic priors from the data, we... | {
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2411.11357 | Text-guided Zero-Shot Object Localization | [
"cs.CV"
] | Object localization is a hot issue in computer vision area, which aims to identify and determine the precise location of specific objects from image or video. Most existing object localization methods heavily rely on extensive labeled data, which are costly to annotate and constrain their applicability. Therefore, we p... | {
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2411.11360 | CCExpert: Advancing MLLM Capability in Remote Sensing Change Captioning
with Difference-Aware Integration and a Foundational Dataset | [
"cs.CV"
] | Remote Sensing Image Change Captioning (RSICC) aims to generate natural language descriptions of surface changes between multi-temporal remote sensing images, detailing the categories, locations, and dynamics of changed objects (e.g., additions or disappearances). Many current methods attempt to leverage the long-seque... | {
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2411.11361 | Scalable Autoregressive Monocular Depth Estimation | [
"cs.CV"
] | This paper shows that the autoregressive model is an effective and scalable monocular depth estimator. Our idea is simple: We tackle the monocular depth estimation (MDE) task with an autoregressive prediction paradigm, based on two core designs. First, our depth autoregressive model (DAR) treats the depth map of differ... | {
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2411.11362 | MAIRA-Seg: Enhancing Radiology Report Generation with Segmentation-Aware
Multimodal Large Language Models | [
"cs.CV",
"cs.CL"
] | There is growing interest in applying AI to radiology report generation, particularly for chest X-rays (CXRs). This paper investigates whether incorporating pixel-level information through segmentation masks can improve fine-grained image interpretation of multimodal large language models (MLLMs) for radiology report g... | {
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2411.11363 | GPS-Gaussian+: Generalizable Pixel-wise 3D Gaussian Splatting for
Real-Time Human-Scene Rendering from Sparse Views | [
"cs.CV"
] | Differentiable rendering techniques have recently shown promising results for free-viewpoint video synthesis of characters. However, such methods, either Gaussian Splatting or neural implicit rendering, typically necessitate per-subject optimization which does not meet the requirement of real-time rendering in an inter... | {
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2411.11364 | Continual Task Learning through Adaptive Policy Self-Composition | [
"cs.LG",
"cs.AI"
] | Training a generalizable agent to continually learn a sequence of tasks from offline trajectories is a natural requirement for long-lived agents, yet remains a significant challenge for current offline reinforcement learning (RL) algorithms. Specifically, an agent must be able to rapidly adapt to new tasks using newly ... | {
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2411.11370 | TL-CLIP: A Power-specific Multimodal Pre-trained Visual Foundation Model
for Transmission Line Defect Recognition | [
"cs.CV"
] | Transmission line defect recognition models have traditionally used general pre-trained weights as the initial basis for their training. These models often suffer weak generalization capability due to the lack of domain knowledge in the pre-training dataset. To address this issue, we propose a two-stage transmission-li... | {
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2411.11371 | Rethinking Thinking Tokens: Understanding Why They Underperform in
Practice | [
"cs.CL",
"cs.LG"
] | Thinking Tokens (TT) have been proposed as an unsupervised method to facilitate reasoning in language models. However, despite their conceptual appeal, our findings show that TTs marginally improves performance and consistently underperforms compared to Chain-of-Thought (CoT) reasoning across multiple benchmarks. We hy... | {
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2411.11374 | LeC$^2$O-NeRF: Learning Continuous and Compact Large-Scale Occupancy for
Urban Scenes | [
"cs.CV",
"cs.GR"
] | In NeRF, a critical problem is to effectively estimate the occupancy to guide empty-space skipping and point sampling. Grid-based methods work well for small-scale scenes. However, on large-scale scenes, they are limited by predefined bounding boxes, grid resolutions, and high memory usage for grid updates, and thus st... | {
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2411.11375 | Graph Neural Networks on Graph Databases | [
"cs.LG",
"cs.DB"
] | Training graph neural networks on large datasets has long been a challenge. Traditional approaches include efficiently representing the whole graph in-memory, designing parameter efficient and sampling-based models, and graph partitioning in a distributed setup. Separately, graph databases with native graph storage and... | {
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2411.11376 | Lung Disease Detection with Vision Transformers: A Comparative Study of
Machine Learning Methods | [
"eess.IV",
"cs.CV"
] | Recent advancements in medical image analysis have predominantly relied on Convolutional Neural Networks (CNNs), achieving impressive performance in chest X-ray classification tasks, such as the 92% AUC reported by AutoThorax-Net and the 88% AUC achieved by ChexNet in classifcation tasks. However, in the medical field,... | {
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2411.11385 | Information Rates of Channels with Additive White Cauchy Noise | [
"cs.IT",
"math.IT"
] | Information transmission over discrete-time channels with memoryless additive noise obeying a Cauchy, rather than Gaussian, distribution, are studied. The channel input satisfies an average power constraint. Upper and lower bounds to such additive white Cauchy noise (AWCN) channel capacity are established. In the high ... | {
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2411.11391 | The GECo algorithm for Graph Neural Networks Explanation | [
"cs.LG",
"cs.AI"
] | Graph Neural Networks (GNNs) are powerful models that can manage complex data sources and their interconnection links. One of GNNs' main drawbacks is their lack of interpretability, which limits their application in sensitive fields. In this paper, we introduce a new methodology involving graph communities to address t... | {
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2411.11394 | InstruGen: Automatic Instruction Generation for Vision-and-Language
Navigation Via Large Multimodal Models | [
"cs.RO"
] | Recent research on Vision-and-Language Navigation (VLN) indicates that agents suffer from poor generalization in unseen environments due to the lack of realistic training environments and high-quality path-instruction pairs. Most existing methods for constructing realistic navigation scenes have high costs, and the ext... | {
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2411.11396 | Stacking Brick by Brick: Aligned Feature Isolation for Incremental Face
Forgery Detection | [
"cs.CV"
] | The rapid advancement of face forgery techniques has introduced a growing variety of forgeries. Incremental Face Forgery Detection (IFFD), involving gradually adding new forgery data to fine-tune the previously trained model, has been introduced as a promising strategy to deal with evolving forgery methods. However, a ... | {
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2411.11405 | Extended Neural Contractive Dynamical Systems: On Multiple Tasks and
Riemannian Safety Regions | [
"cs.RO",
"cs.LG"
] | Stability guarantees are crucial when ensuring that a fully autonomous robot does not take undesirable or potentially harmful actions. We recently proposed the Neural Contractive Dynamical Systems (NCDS), which is a neural network architecture that guarantees contractive stability. With this, learning-from-demonstratio... | {
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2411.11406 | Bridging the Resource Gap: Deploying Advanced Imitation Learning Models
onto Affordable Embedded Platforms | [
"cs.LG",
"cs.RO"
] | Advanced imitation learning with structures like the transformer is increasingly demonstrating its advantages in robotics. However, deploying these large-scale models on embedded platforms remains a major challenge. In this paper, we propose a pipeline that facilitates the migration of advanced imitation learning algor... | {
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2411.11407 | The Dark Side of Trust: Authority Citation-Driven Jailbreak Attacks on
Large Language Models | [
"cs.LG"
] | The widespread deployment of large language models (LLMs) across various domains has showcased their immense potential while exposing significant safety vulnerabilities. A major concern is ensuring that LLM-generated content aligns with human values. Existing jailbreak techniques reveal how this alignment can be compro... | {
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2411.11409 | IKEA Manuals at Work: 4D Grounding of Assembly Instructions on Internet
Videos | [
"cs.CV",
"cs.AI",
"cs.LG",
"cs.RO"
] | Shape assembly is a ubiquitous task in daily life, integral for constructing complex 3D structures like IKEA furniture. While significant progress has been made in developing autonomous agents for shape assembly, existing datasets have not yet tackled the 4D grounding of assembly instructions in videos, essential for a... | {
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2411.11411 | Distributed Learning with Partial Information Sharing | [
"eess.SY",
"cs.SY"
] | This work studies the distributed learning process on a network of agents. Agents make partial observation about an unknown hypothesis and iteratively share their beliefs over a set of possible hypotheses with their neighbors to learn the true hypothesis. We present and analyze a distributed learning algorithm in which... | {
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2411.11414 | Temporal and Spatial Reservoir Ensembling Techniques for Liquid State
Machines | [
"cs.LG",
"cs.NE"
] | Reservoir computing (RC), is a class of computational methods such as Echo State Networks (ESN) and Liquid State Machines (LSM) describe a generic method to perform pattern recognition and temporal analysis with any non-linear system. This is enabled by Reservoir Computing being a shallow network model with only Input,... | {
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2411.11421 | Enabling DBSCAN for Very Large-Scale High-Dimensional Spaces | [
"cs.CV"
] | DBSCAN is one of the most important non-parametric unsupervised data analysis tools. By applying DBSCAN to a dataset, two key analytical results can be obtained: (1) clustering data points based on density distribution and (2) identifying outliers in the dataset. However, the time complexity of the DBSCAN algorithm is ... | {
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2411.11424 | Membership Inference Attack against Long-Context Large Language Models | [
"cs.CL"
] | Recent advances in Large Language Models (LLMs) have enabled them to overcome their context window limitations, and demonstrate exceptional retrieval and reasoning capacities on longer context. Quesion-answering systems augmented with Long-Context Language Models (LCLMs) can automatically search massive external data a... | {
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2411.11435 | GLDesigner: Leveraging Multi-Modal LLMs as Designer for Enhanced
Aesthetic Text Glyph Layouts | [
"cs.CV"
] | Text logo design heavily relies on the creativity and expertise of professional designers, in which arranging element layouts is one of the most important procedures. However, few attention has been paid to this specific task which needs to take precise textural details and user constraints into consideration, but only... | {
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2411.11436 | Implicit Regularization for Multi-label Feature Selection | [
"cs.LG",
"cs.AI"
] | In this paper, we address the problem of feature selection in the context of multi-label learning, by using a new estimator based on implicit regularization and label embedding. Unlike the sparse feature selection methods that use a penalized estimator with explicit regularization terms such as $l_{2,1}$-norm, MCP or S... | {
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2411.11437 | Causal Effect of Group Diversity on Redundancy and Coverage in
Peer-Reviewing | [
"cs.DL",
"cs.CL",
"stat.AP"
] | A large host of scientific journals and conferences solicit peer reviews from multiple reviewers for the same submission, aiming to gather a broader range of perspectives and mitigate individual biases. In this work, we reflect on the role of diversity in the slate of reviewers assigned to evaluate a submitted paper as... | {
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2411.11448 | Unveiling the Inflexibility of Adaptive Embedding in Traffic Forecasting | [
"cs.LG",
"cs.AI"
] | Spatiotemporal Graph Neural Networks (ST-GNNs) and Transformers have shown significant promise in traffic forecasting by effectively modeling temporal and spatial correlations. However, rapid urbanization in recent years has led to dynamic shifts in traffic patterns and travel demand, posing major challenges for accura... | {
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2411.11451 | Robust Markov Decision Processes: A Place Where AI and Formal Methods
Meet | [
"cs.AI",
"math.OC"
] | Markov decision processes (MDPs) are a standard model for sequential decision-making problems and are widely used across many scientific areas, including formal methods and artificial intelligence (AI). MDPs do, however, come with the restrictive assumption that the transition probabilities need to be precisely known. ... | {
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2411.11453 | Fluid Antenna-Aided Rate-Splitting Multiple Access | [
"cs.IT",
"eess.SP",
"math.IT"
] | This letter considers a fluid antenna system (FAS)-aided rate-splitting multiple access (RSMA) approach for downlink transmission. In particular, a base station (BS) equipped with a single traditional antenna system (TAS) uses RSMA signaling to send information to several mobile users (MUs) each equipped with FAS. To u... | {
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2411.11454 | Relevance-guided Audio Visual Fusion for Video Saliency Prediction | [
"cs.CV"
] | Audio data, often synchronized with video frames, plays a crucial role in guiding the audience's visual attention. Incorporating audio information into video saliency prediction tasks can enhance the prediction of human visual behavior. However, existing audio-visual saliency prediction methods often directly fuse audi... | {
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2411.11455 | The ADUULM-360 Dataset -- A Multi-Modal Dataset for Depth Estimation in
Adverse Weather | [
"cs.CV"
] | Depth estimation is an essential task toward full scene understanding since it allows the projection of rich semantic information captured by cameras into 3D space. While the field has gained much attention recently, datasets for depth estimation lack scene diversity or sensor modalities. This work presents the ADUULM-... | {
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2411.11457 | Upside-Down Reinforcement Learning for More Interpretable Optimal
Control | [
"cs.LG"
] | Model-Free Reinforcement Learning (RL) algorithms either learn how to map states to expected rewards or search for policies that can maximize a certain performance function. Model-Based algorithms instead, aim to learn an approximation of the underlying model of the RL environment and then use it in combination with pl... | {
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2411.11458 | HistoEncoder: a digital pathology foundation model for prostate cancer | [
"eess.IV",
"cs.AI",
"cs.CV"
] | Foundation models are trained on massive amounts of data to distinguish complex patterns and can be adapted to a wide range of downstream tasks with minimal computational resources. Here, we develop a foundation model for prostate cancer digital pathology called HistoEncoder by pre-training on 48 million prostate tissu... | {
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2411.11464 | PALMS: Parallel Adaptive Lasso with Multi-directional Signals for Latent
Networks Reconstruction | [
"math.ST",
"cs.LG",
"stat.ML",
"stat.TH"
] | Large-scale networks exist in many field and play an important role in real-world dynamics. However, the networks are usually latent and expensive to detect, which becomes the main challenging for many applications and empirical analysis. Several statistical methods were proposed to infer the edges, but the complexity ... | {
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2411.11465 | Re-examining learning linear functions in context | [
"cs.LG",
"cs.CL"
] | In-context learning (ICL) has emerged as a powerful paradigm for easily adapting Large Language Models (LLMs) to various tasks. However, our understanding of how ICL works remains limited. We explore a simple model of ICL in a controlled setup with synthetic training data to investigate ICL of univariate linear functio... | {
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2411.11466 | MGNiceNet: Unified Monocular Geometric Scene Understanding | [
"cs.CV"
] | Monocular geometric scene understanding combines panoptic segmentation and self-supervised depth estimation, focusing on real-time application in autonomous vehicles. We introduce MGNiceNet, a unified approach that uses a linked kernel formulation for panoptic segmentation and self-supervised depth estimation. MGNiceNe... | {
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2411.11467 | Physics meets Topology: Physics-informed topological neural networks for
learning rigid body dynamics | [
"cs.LG"
] | Rigid body interactions are fundamental to numerous scientific disciplines, but remain challenging to simulate due to their abrupt nonlinear nature and sensitivity to complex, often unknown environmental factors. These challenges call for adaptable learning-based methods capable of capturing complex interactions beyond... | {
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2411.11468 | $\nu$-LPA: Fast GPU-based Label Propagation Algorithm (LPA) for
Community Detection | [
"cs.DC",
"cs.SI"
] | Community detection is the problem of identifying natural divisions in networks. Efficient parallel algorithms for identifying such divisions are critical in a number of applications. This report presents an optimized implementation of the Label Propagation Algorithm (LPA) for community detection, featuring an asynchro... | {
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2411.11471 | Generalizable Person Re-identification via Balancing Alignment and
Uniformity | [
"cs.CV"
] | Domain generalizable person re-identification (DG re-ID) aims to learn discriminative representations that are robust to distributional shifts. While data augmentation is a straightforward solution to improve generalization, certain augmentations exhibit a polarized effect in this task, enhancing in-distribution perfor... | {
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2411.11474 | Graph Neural Networks for Quantifying Compatibility Mechanisms in
Traditional Chinese Medicine | [
"cs.LG",
"q-bio.QM"
] | Traditional Chinese Medicine (TCM) involves complex compatibility mechanisms characterized by multi-component and multi-target interactions, which are challenging to quantify. To address this challenge, we applied graph artificial intelligence to develop a TCM multi-dimensional knowledge graph that bridges traditional ... | {
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2411.11475 | MVLight: Relightable Text-to-3D Generation via Light-conditioned
Multi-View Diffusion | [
"cs.CV"
] | Recent advancements in text-to-3D generation, building on the success of high-performance text-to-image generative models, have made it possible to create imaginative and richly textured 3D objects from textual descriptions. However, a key challenge remains in effectively decoupling light-independent and lighting-depen... | {
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2411.11477 | SL-YOLO: A Stronger and Lighter Drone Target Detection Model | [
"cs.CV"
] | Detecting small objects in complex scenes, such as those captured by drones, is a daunting challenge due to the difficulty in capturing the complex features of small targets. While the YOLO family has achieved great success in large target detection, its performance is less than satisfactory when faced with small targe... | {
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2411.11479 | Value-Spectrum: Quantifying Preferences of Vision-Language Models via
Value Decomposition in Social Media Contexts | [
"cs.CL"
] | The recent progress in Vision-Language Models (VLMs) has broadened the scope of multimodal applications. However, evaluations often remain limited to functional tasks, neglecting abstract dimensions such as personality traits and human values. To address this gap, we introduce Value-Spectrum, a novel Visual Question An... | {
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2411.11481 | Exploring Emerging Trends and Research Opportunities in Visual Place
Recognition | [
"cs.CV",
"cs.RO"
] | Visual-based recognition, e.g., image classification, object detection, etc., is a long-standing challenge in computer vision and robotics communities. Concerning the roboticists, since the knowledge of the environment is a prerequisite for complex navigation tasks, visual place recognition is vital for most localizati... | {
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2411.11483 | Robust State Estimation for Legged Robots with Dual Beta Kalman Filter | [
"cs.RO"
] | Existing state estimation algorithms for legged robots that rely on proprioceptive sensors often overlook foot slippage and leg deformation in the physical world, leading to large estimation errors. To address this limitation, we propose a comprehensive measurement model that accounts for both foot slippage and variabl... | {
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2411.11487 | Look a Group at Once: Multi-Slide Modeling for Survival Prediction | [
"cs.CV"
] | Survival prediction is a critical task in pathology. In clinical practice, pathologists often examine multiple cases, leveraging a broader spectrum of cancer phenotypes to enhance pathological assessment. Despite significant advancements in deep learning, current solutions typically model each slide as a sample, strugg... | {
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2411.11494 | Alien Recombination: Exploring Concept Blends Beyond Human Cognitive
Availability in Visual Art | [
"cs.AI",
"cs.CY",
"cs.LG"
] | While AI models have demonstrated remarkable capabilities in constrained domains like game strategy, their potential for genuine creativity in open-ended domains like art remains debated. We explore this question by examining how AI can transcend human cognitive limitations in visual art creation. Our research hypothes... | {
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2411.11496 | Safe + Safe = Unsafe? Exploring How Safe Images Can Be Exploited to
Jailbreak Large Vision-Language Models | [
"cs.CL"
] | Recent advances in Large Vision-Language Models (LVLMs) have showcased strong reasoning abilities across multiple modalities, achieving significant breakthroughs in various real-world applications. Despite this great success, the safety guardrail of LVLMs may not cover the unforeseen domains introduced by the visual mo... | {
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2411.11497 | Physics Encoded Blocks in Residual Neural Network Architectures for
Digital Twin Models | [
"cs.LG",
"cs.RO"
] | Physics Informed Machine Learning has emerged as a popular approach in modelling and simulation for digital twins to generate accurate models of processes and behaviours of real-world systems. However, despite their success in generating accurate and reliable models, the existing methods either use simple regularizatio... | {
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2411.11500 | Timescale-agnostic characterisation for collective attention events | [
"cs.SI",
"cs.CY"
] | Online communications, and in particular social media, are a key component of how society interacts with and promotes content online. Collective attention on such content can vary wildly. The majority of breaking topics quickly fade into obscurity after only a handful of interactions, while the possibility exists for c... | {
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2411.11502 | All-domain Moveline Evolution Network for Click-Through Rate Prediction | [
"cs.IR"
] | E-commerce app users exhibit behaviors that are inherently logically consistent. A series of multi-scenario user behaviors interconnect to form the scene-level all-domain user moveline, which ultimately reveals the user's true intention. Traditional CTR prediction methods typically focus on the item-level interaction b... | {
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2411.11504 | Search, Verify and Feedback: Towards Next Generation Post-training
Paradigm of Foundation Models via Verifier Engineering | [
"cs.AI",
"cs.CL",
"stat.ML"
] | The evolution of machine learning has increasingly prioritized the development of powerful models and more scalable supervision signals. However, the emergence of foundation models presents significant challenges in providing effective supervision signals necessary for further enhancing their capabilities. Consequently... | {
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2411.11505 | LaVin-DiT: Large Vision Diffusion Transformer | [
"cs.CV"
] | This paper presents the Large Vision Diffusion Transformer (LaVin-DiT), a scalable and unified foundation model designed to tackle over 20 computer vision tasks in a generative framework. Unlike existing large vision models directly adapted from natural language processing architectures, which rely on less efficient au... | {
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2411.11507 | SignEye: Traffic Sign Interpretation from Vehicle First-Person View | [
"cs.CV"
] | Traffic signs play a key role in assisting autonomous driving systems (ADS) by enabling the assessment of vehicle behavior in compliance with traffic regulations and providing navigation instructions. However, current works are limited to basic sign understanding without considering the egocentric vehicle's spatial pos... | {
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2411.11508 | Collaborative Contrastive Network for Click-Through Rate Prediction | [
"cs.IR"
] | E-commerce platforms provide entrances for customers to enter mini-apps to meet their specific shopping needs. At the entrance of a mini-app, a trigger item recommended based on customers' historical preferences, is displayed to attract customers to enter the mini-app. Existing Click-Through Rate (CTR) prediction appro... | {
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2411.11510 | Closed-loop multi-step planning with innate physics knowledge | [
"cs.RO",
"cs.AI",
"cs.ET",
"cs.SY",
"eess.SY"
] | We present a hierarchical framework to solve robot planning as an input control problem. At the lowest level are temporary closed control loops, ("tasks"), each representing a behaviour, contingent on a specific sensory input and therefore temporary. At the highest level, a supervising "Configurator" directs task creat... | {
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2411.11511 | Structure learning with Temporal Gaussian Mixture for model-based
Reinforcement Learning | [
"cs.LG",
"cs.AI",
"stat.ML"
] | Model-based reinforcement learning refers to a set of approaches capable of sample-efficient decision making, which create an explicit model of the environment. This model can subsequently be used for learning optimal policies. In this paper, we propose a temporal Gaussian Mixture Model composed of a perception model a... | {
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2411.11513 | A Modular Open Source Framework for Genomic Variant Calling | [
"q-bio.QM",
"cs.LG"
] | Variant calling is a fundamental task in genomic research, essential for detecting genetic variations such as single nucleotide polymorphisms (SNPs) and insertions or deletions (indels). This paper presents an enhancement to DeepChem, a widely used open-source drug discovery framework, through the integration of DeepVa... | {
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2411.11514 | Learning a Neural Association Network for Self-supervised Multi-Object
Tracking | [
"cs.CV"
] | This paper introduces a novel framework to learn data association for multi-object tracking in a self-supervised manner. Fully-supervised learning methods are known to achieve excellent tracking performances, but acquiring identity-level annotations is tedious and time-consuming. Motivated by the fact that in real-worl... | {
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2411.11515 | Cascaded Diffusion Models for 2D and 3D Microscopy Image Synthesis to
Enhance Cell Segmentation | [
"cs.CV",
"cs.LG"
] | Automated cell segmentation in microscopy images is essential for biomedical research, yet conventional methods are labor-intensive and prone to error. While deep learning-based approaches have proven effective, they often require large annotated datasets, which are scarce due to the challenges of manual annotation. To... | {
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2411.11516 | Efficient Sample-optimal Learning of Gaussian Tree Models via
Sample-optimal Testing of Gaussian Mutual Information | [
"cs.LG",
"cs.DS",
"stat.ML"
] | Learning high-dimensional distributions is a significant challenge in machine learning and statistics. Classical research has mostly concentrated on asymptotic analysis of such data under suitable assumptions. While existing works [Bhattacharyya et al.: SICOMP 2023, Daskalakis et al.: STOC 2021, Choo et al.: ALT 2024] ... | {
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2411.11520 | A Pre-Trained Graph-Based Model for Adaptive Sequencing of Educational
Documents | [
"cs.AI",
"cs.CY",
"cs.LG"
] | Massive Open Online Courses (MOOCs) have greatly contributed to making education more accessible. However, many MOOCs maintain a rigid, one-size-fits-all structure that fails to address the diverse needs and backgrounds of individual learners. Learning path personalization aims to address this limitation, by tailoring ... | {
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2411.11521 | Preempting Text Sanitization Utility in Resource-Constrained
Privacy-Preserving LLM Interactions | [
"cs.CR",
"cs.LG"
] | Individuals have been increasingly interacting with online Large Language Models (LLMs), both in their work and personal lives. These interactions raise privacy issues as the LLMs are typically hosted by third-parties who can gather a variety of sensitive information about users and their companies. Text Sanitization t... | {
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2411.11525 | Reliable Poisoned Sample Detection against Backdoor Attacks Enhanced by
Sharpness Aware Minimization | [
"cs.CV"
] | Backdoor attack has been considered as a serious security threat to deep neural networks (DNNs). Poisoned sample detection (PSD) that aims at filtering out poisoned samples from an untrustworthy training dataset has shown very promising performance for defending against data poisoning based backdoor attacks. However, w... | {
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2411.11530 | SeqProFT: Applying LoRA Finetuning for Sequence-only Protein Property
Predictions | [
"cs.LG",
"q-bio.QM"
] | Protein language models (PLMs) are capable of learning the relationships between protein sequences and functions by treating amino acid sequences as textual data in a self-supervised manner. However, fine-tuning these models typically demands substantial computational resources and time, with results that may not alway... | {
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} |
2411.11531 | Addressing Hallucinations in Language Models with Knowledge Graph
Embeddings as an Additional Modality | [
"cs.CL",
"cs.AI"
] | In this paper we present an approach to reduce hallucinations in Large Language Models (LLMs) by incorporating Knowledge Graphs (KGs) as an additional modality. Our method involves transforming input text into a set of KG embeddings and using an adapter to integrate these embeddings into the language model space, witho... | {
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} |
2411.11536 | Hierarchical-Graph-Structured Edge Partition Models for Learning
Evolving Community Structure | [
"cs.SI",
"cs.LG"
] | We propose a novel dynamic network model to capture evolving latent communities within temporal networks. To achieve this, we decompose each observed dynamic edge between vertices using a Poisson-gamma edge partition model, assigning each vertex to one or more latent communities through \emph{nonnegative} vertex-commun... | {
"Other": 0,
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"cs.SD": 0,
"cs.SI": 1,
"cs.SY": 0
} |
2411.11539 | Channel Capacity-Aware Distributed Encoding for Multi-View Sensing and
Edge Inference | [
"cs.IT",
"eess.SP",
"math.IT"
] | Integrated sensing and communication (ISAC) unifies wireless communication and sensing by sharing spectrum and hardware, which often incurs trade-offs between two functions due to limited resources. However, this paper shifts focus to exploring the synergy between communication and sensing, using WiFi sensing as an exe... | {
"Other": 0,
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"cs.SI": 0,
"cs.SY": 0
} |
2411.11542 | Data-Driven Structured Robust Control of Linear Systems | [
"math.OC",
"cs.SY",
"eess.SY"
] | Static structured control refers to the task of designing a state-feedback controller such that the control gain satisfies a subspace constraint. Structured control has applications in control of communication-inhibited dynamical systems, such as systems in networked environments. This work performs $H_2$-suboptimal re... | {
"Other": 0,
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"cs.SY": 1
} |
2411.11543 | PSA-VLM: Enhancing Vision-Language Model Safety through Progressive
Concept-Bottleneck-Driven Alignment | [
"cs.CV",
"cs.AI"
] | Benefiting from the powerful capabilities of Large Language Models (LLMs), pre-trained visual encoder models connected to LLMs form Vision Language Models (VLMs). However, recent research shows that the visual modality in VLMs is highly vulnerable, allowing attackers to bypass safety alignment in LLMs through visually ... | {
"Other": 0,
"cs.AI": 1,
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"cs.CR": 0,
"cs.CV": 1,
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"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.11545 | An Efficient Multicast Addressing Encoding Scheme for Multi-Core
Neuromorphic Processors | [
"cs.AR",
"cs.NE"
] | Multi-core neuromorphic processors are becoming increasingly significant due to their energy-efficient local computing and scalable modular architecture, particularly for event-based processing applications. However, minimizing the cost of inter-core communication, which accounts for the majority of energy usage, remai... | {
"Other": 1,
"cs.AI": 0,
"cs.CE": 0,
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"cs.CR": 0,
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"cs.MA": 0,
"cs.NE": 1,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.11548 | Real-Time Fitness Exercise Classification and Counting from Video Frames | [
"cs.CV",
"cs.AI",
"cs.LG"
] | This paper introduces a novel method for real-time exercise classification using a Bidirectional Long Short-Term Memory (BiLSTM) neural network. Existing exercise recognition approaches often rely on synthetic datasets, raw coordinate inputs sensitive to user and camera variations, and fail to fully exploit the tempora... | {
"Other": 0,
"cs.AI": 1,
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"cs.NE": 0,
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"cs.SD": 0,
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"cs.SY": 0
} |
2411.11549 | Sound Value Iteration for Simple Stochastic Games | [
"cs.GT",
"cs.LO",
"cs.SY",
"eess.SY"
] | Algorithmic analysis of Markov decision processes (MDP) and stochastic games (SG) in practice relies on value-iteration (VI) algorithms. Since the basic version of VI does not provide guarantees on the precision of the result, variants of VI have been proposed that offer such guarantees. In particular, sound value iter... | {
"Other": 1,
"cs.AI": 0,
"cs.CE": 0,
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"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 1
} |
2411.11556 | Data-driven model reconstruction for nonlinear wave dynamics | [
"physics.optics",
"cs.LG",
"math-ph",
"math.MP",
"physics.data-an"
] | The use of machine learning to predict wave dynamics is a topic of growing interest, but commonly-used deep learning approaches suffer from a lack of interpretability of the trained models. Here we present an interpretable machine learning framework for analyzing the nonlinear evolution dynamics of optical wavepackets ... | {
"Other": 0,
"cs.AI": 0,
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"cs.CR": 0,
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"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
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