id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2412.06312 | Towards High-Level Modelling in Automated Planning | [
"cs.AI"
] | Planning is a fundamental activity, arising frequently in many contexts, from daily tasks to industrial processes. The planning task consists of selecting a sequence of actions to achieve a specified goal from specified initial conditions. The Planning Domain Definition Language (PDDL) is the leading language used in t... | {
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2412.06313 | Vision-Based Deep Reinforcement Learning of UAV Autonomous Navigation
Using Privileged Information | [
"cs.RO",
"cs.CV",
"cs.LG"
] | The capability of UAVs for efficient autonomous navigation and obstacle avoidance in complex and unknown environments is critical for applications in agricultural irrigation, disaster relief and logistics. In this paper, we propose the DPRL (Distributed Privileged Reinforcement Learning) navigation algorithm, an end-to... | {
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2412.06314 | CAD-Unet: A Capsule Network-Enhanced Unet Architecture for Accurate
Segmentation of COVID-19 Lung Infections from CT Images | [
"eess.IV",
"cs.AI",
"cs.CV"
] | Since the outbreak of the COVID-19 pandemic in 2019, medical imaging has emerged as a primary modality for diagnosing COVID-19 pneumonia. In clinical settings, the segmentation of lung infections from computed tomography images enables rapid and accurate quantification and diagnosis of COVID-19. Segmentation of COVID-1... | {
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2412.06322 | LLaVA-SpaceSGG: Visual Instruct Tuning for Open-vocabulary Scene Graph
Generation with Enhanced Spatial Relations | [
"cs.CV"
] | Scene Graph Generation (SGG) converts visual scenes into structured graph representations, providing deeper scene understanding for complex vision tasks. However, existing SGG models often overlook essential spatial relationships and struggle with generalization in open-vocabulary contexts. To address these limitations... | {
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2412.06323 | HAIFAI: Human-AI Collaboration for Mental Face Reconstruction | [
"cs.CV"
] | We present HAIFAI - a novel collaborative human-AI system to tackle the challenging task of reconstructing a visual representation of a face that exists only in a person's mind. Users iteratively rank images presented by the AI system based on their resemblance to a mental image. These rankings, in turn, allow the syst... | {
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2412.06324 | World knowledge-enhanced Reasoning Using Instruction-guided Interactor
in Autonomous Driving | [
"cs.CV"
] | The Multi-modal Large Language Models (MLLMs) with extensive world knowledge have revitalized autonomous driving, particularly in reasoning tasks within perceivable regions. However, when faced with perception-limited areas (dynamic or static occlusion regions), MLLMs struggle to effectively integrate perception abilit... | {
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2412.06327 | Robust Output Tracking for an Uncertain and Nonlinear 3D PDE-ODE System:
Preventing Induced Seismicity in Underground Reservoirs | [
"eess.SY",
"cs.SY",
"math.OC"
] | This paper presents a robust control strategy for output tracking of a nonlinear 3D PDE-ODE system. The output feedback control was developed by bounding the solution and its time derivative for both the infinite-dimensional system and the nonlinear ODE, and leveraging these bounds to ensure the boundedness of the cont... | {
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2412.06329 | Normalizing Flows are Capable Generative Models | [
"cs.CV",
"cs.LG"
] | Normalizing Flows (NFs) are likelihood-based models for continuous inputs. They have demonstrated promising results on both density estimation and generative modeling tasks, but have received relatively little attention in recent years. In this work, we demonstrate that NFs are more powerful than previously believed. W... | {
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2412.06332 | Not All Errors Are Equal: Investigation of Speech Recognition Errors in
Alzheimer's Disease Detection | [
"cs.CL",
"cs.AI",
"cs.LG",
"q-bio.NC"
] | Automatic Speech Recognition (ASR) plays an important role in speech-based automatic detection of Alzheimer's disease (AD). However, recognition errors could propagate downstream, potentially impacting the detection decisions. Recent studies have revealed a non-linear relationship between word error rates (WER) and AD ... | {
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2412.06333 | Augmenting the action space with conventions to improve multi-agent
cooperation in Hanabi | [
"cs.MA",
"cs.AI",
"cs.LG"
] | The card game Hanabi is considered a strong medium for the testing and development of multi-agent reinforcement learning (MARL) algorithms, due to its cooperative nature, hidden information, limited communication and remarkable complexity. Previous research efforts have explored the capabilities of MARL algorithms with... | {
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2412.06334 | TriDi: Trilateral Diffusion of 3D Humans, Objects, and Interactions | [
"cs.CV"
] | Modeling 3D human-object interaction (HOI) is a problem of great interest for computer vision and a key enabler for virtual and mixed-reality applications. Existing methods work in a one-way direction: some recover plausible human interactions conditioned on a 3D object; others recover the object pose conditioned on a ... | {
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2412.06335 | StructRide: A Framework to Exploit the Structure Information of
Shareability Graph in Ridesharing | [
"cs.DB"
] | Ridesharing services play an essential role in modern transportation, which significantly reduces traffic congestion and exhaust pollution. In the ridesharing problem, improving the sharing rate between riders can not only save the travel cost of drivers but also utilize vehicle resources more efficiently. The existing... | {
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2412.06340 | UniPaint: Unified Space-time Video Inpainting via Mixture-of-Experts | [
"cs.CV"
] | In this paper, we present UniPaint, a unified generative space-time video inpainting framework that enables spatial-temporal inpainting and interpolation. Different from existing methods that treat video inpainting and video interpolation as two distinct tasks, we leverage a unified inpainting framework to tackle them ... | {
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2412.06341 | Elastic-DETR: Making Image Resolution Learnable with Content-Specific
Network Prediction | [
"cs.CV",
"cs.AI"
] | Multi-scale image resolution is a de facto standard approach in modern object detectors, such as DETR. This technique allows for the acquisition of various scale information from multiple image resolutions. However, manual hyperparameter selection of the resolution can restrict its flexibility, which is informed by pri... | {
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2412.06342 | Tracking control of latent dynamic systems with application to
spacecraft attitude control | [
"eess.SY",
"cs.LG",
"cs.SY"
] | When intelligent spacecraft or space robots perform tasks in a complex environment, the controllable variables are usually not directly available and have to be inferred from high-dimensional observable variables, such as outputs of neural networks or images. While the dynamics of these observations are highly complex,... | {
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2412.06352 | SeFENet: Robust Deep Homography Estimation via Semantic-Driven Feature
Enhancement | [
"cs.CV"
] | Images captured in harsh environments often exhibit blurred details, reduced contrast, and color distortion, which hinder feature detection and matching, thereby affecting the accuracy and robustness of homography estimation. While visual enhancement can improve contrast and clarity, it may introduce visual-tolerant ar... | {
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2412.06353 | 3D Extended Target Sensing in ISAC: Cram\'er-Rao Bound Analysis and
Beamforming Design | [
"cs.IT",
"eess.SP",
"math.IT"
] | This paper investigates an integrated sensing and communication (ISAC) system where the sensing target is a three-dimensional (3D) extended target, for which multiple scatterers from the target surface can be resolved. We first introduce a second-order truncated Fourier series surface model for an arbitrarily-shaped 3D... | {
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2412.06354 | GraphNeuralNetworks.jl: Deep Learning on Graphs with Julia | [
"cs.LG"
] | GraphNeuralNetworks.jl is an open-source framework for deep learning on graphs, written in the Julia programming language. It supports multiple GPU backends, generic sparse or dense graph representations, and offers convenient interfaces for manipulating standard, heterogeneous, and temporal graphs with attributes at t... | {
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2412.06355 | Flexible and Scalable Deep Dendritic Spiking Neural Networks with
Multiple Nonlinear Branching | [
"cs.NE"
] | Recent advances in spiking neural networks (SNNs) have a predominant focus on network architectures, while relatively little attention has been paid to the underlying neuron model. The point neuron models, a cornerstone of deep SNNs, pose a bottleneck on the network-level expressivity since they depict somatic dynamics... | {
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2412.06359 | On-Device Self-Supervised Learning of Low-Latency Monocular Depth from
Only Events | [
"cs.RO",
"cs.CV"
] | Event cameras provide low-latency perception for only milliwatts of power. This makes them highly suitable for resource-restricted, agile robots such as small flying drones. Self-supervised learning based on contrast maximization holds great potential for event-based robot vision, as it foregoes the need to high-freque... | {
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2412.06365 | Is Self-Supervision Enough? Benchmarking Foundation Models Against
End-to-End Training for Mitotic Figure Classification | [
"cs.CV"
] | Foundation models (FMs), i.e., models trained on a vast amount of typically unlabeled data, have become popular and available recently for the domain of histopathology. The key idea is to extract semantically rich vectors from any input patch, allowing for the use of simple subsequent classification networks potentiall... | {
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2412.06368 | Measuring Pre-training Data Quality without Labels for Time Series
Foundation Models | [
"cs.LG",
"cs.AI",
"stat.ML"
] | Recently, there has been a growing interest in time series foundation models that generalize across different downstream tasks. A key to strong foundation models is a diverse pre-training dataset, which is particularly challenging to collect for time series classification. In this work, we explore the performance of a ... | {
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2412.06370 | Exploring Memorization and Copyright Violation in Frontier LLMs: A Study
of the New York Times v. OpenAI 2023 Lawsuit | [
"cs.LG",
"cs.AI"
] | Copyright infringement in frontier LLMs has received much attention recently due to the New York Times v. OpenAI lawsuit, filed in December 2023. The New York Times claims that GPT-4 has infringed its copyrights by reproducing articles for use in LLM training and by memorizing the inputs, thereby publicly displaying th... | {
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2412.06380 | Low-Rank Matrix Factorizations with Volume-based Constraints and
Regularizations | [
"cs.LG",
"eess.SP"
] | Low-rank matrix factorizations are a class of linear models widely used in various fields such as machine learning, signal processing, and data analysis. These models approximate a matrix as the product of two smaller matrices, where the left matrix captures latent features while the right matrix linearly decomposes th... | {
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2412.06381 | Gentle robustness implies Generalization | [
"cs.LG",
"stat.ML"
] | Robustness and generalization ability of machine learning models are of utmost importance in various application domains. There is a wide interest in efficient ways to analyze those properties. One important direction is to analyze connection between those two properties. Prior theories suggest that a robust learning a... | {
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2412.06382 | PyPulse: A Python Library for Biosignal Imputation | [
"cs.LG",
"cs.SE"
] | We introduce PyPulse, a Python package for imputation of biosignals in both clinical and wearable sensor settings. Missingness is commonplace in these settings and can arise from multiple causes, such as insecure sensor attachment or data transmission loss. PyPulse's framework provides a modular and extendable framewor... | {
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2412.06388 | Sparse Identification of Nonlinear Dynamics-based Model Predictive
Control for Multirotor Collision Avoidance | [
"cs.RO",
"math.OC"
] | This paper proposes a data-driven model predictive control for multirotor collision avoidance considering uncertainty and an unknown model from a payload. To address this challenge, sparse identification of nonlinear dynamics (SINDy) is used to obtain the governing equation of the multirotor system. The SINDy can disco... | {
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2412.06389 | Exploring the Impact of Synthetic Data on Human Gesture Recognition
Tasks Using GANs | [
"cs.LG"
] | In the evolving domain of Human Activity Recognition (HAR) using Internet of Things (IoT) devices, there is an emerging interest in employing Deep Generative Models (DGMs) to address data scarcity, enhance data quality, and improve classification metrics scores. Among these types of models, Generative Adversarial Netwo... | {
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2412.06390 | Edge Delayed Deep Deterministic Policy Gradient: efficient continuous
control for edge scenarios | [
"cs.LG",
"cs.AI"
] | Deep Reinforcement Learning is gaining increasing attention thanks to its capability to learn complex policies in high-dimensional settings. Recent advancements utilize a dual-network architecture to learn optimal policies through the Q-learning algorithm. However, this approach has notable drawbacks, such as an overes... | {
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2412.06394 | GameArena: Evaluating LLM Reasoning through Live Computer Games | [
"cs.AI",
"cs.CL"
] | Evaluating the reasoning abilities of large language models (LLMs) is challenging. Existing benchmarks often depend on static datasets, which are vulnerable to data contamination and may get saturated over time, or on binary live human feedback that conflates reasoning with other abilities. As the most prominent dynami... | {
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2412.06401 | Memory-Based Control with Event-Triggered Protocol for interval type-2
fuzzy network system under fading channel | [
"eess.SY",
"cs.SY"
] | To address the challenges in networked environments and control problems associated with complex nonlinear uncertain systems, this paper investigates the design of a membership-function-dependent (MFD) memory output-feedback (MOF) controller for interval type-2 (IT2) fuzzy systems under fading channels, leveraging a me... | {
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2412.06403 | Generative Lines Matching Models | [
"cs.CV"
] | In this paper we identify the source of a singularity in the training loss of key denoising models, that causes the denoiser's predictions to collapse towards the mean of the source or target distributions. This degeneracy creates false basins of attraction, distorting the denoising trajectories and ultimately increasi... | {
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2412.06405 | Parameter Adjustments in POMDP-Based Trajectory Planning for
Unsignalized Intersections | [
"cs.RO"
] | This paper investigates the problem of trajectory planning for autonomous vehicles at unsignalized intersections, specifically focusing on scenarios where the vehicle lacks the right of way and yet must cross safely. To address this issue, we have employed a method based on the Partially Observable Markov Decision Proc... | {
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2412.06410 | BatchTopK Sparse Autoencoders | [
"cs.LG",
"cs.AI",
"stat.ML"
] | Sparse autoencoders (SAEs) have emerged as a powerful tool for interpreting language model activations by decomposing them into sparse, interpretable features. A popular approach is the TopK SAE, that uses a fixed number of the most active latents per sample to reconstruct the model activations. We introduce BatchTopK ... | {
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2412.06412 | StarWhisper Telescope: Agent-Based Observation Assistant System to
Approach AI Astrophysicist | [
"astro-ph.IM",
"cs.AI",
"cs.CL"
] | With the rapid advancements in Large Language Models (LLMs), LLM-based agents have introduced convenient and user-friendly methods for leveraging tools across various domains. In the field of astronomical observation, the construction of new telescopes has significantly increased astronomers' workload. Deploying LLM-po... | {
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2412.06413 | World-Consistent Data Generation for Vision-and-Language Navigation | [
"cs.CV"
] | Vision-and-Language Navigation (VLN) is a challenging task that requires an agent to navigate through photorealistic environments following natural-language instructions. One main obstacle existing in VLN is data scarcity, leading to poor generalization performance over unseen environments. Tough data argumentation is ... | {
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2412.06414 | Federated Split Learning with Model Pruning and Gradient Quantization in
Wireless Networks | [
"cs.LG",
"cs.DC",
"cs.NI"
] | As a paradigm of distributed machine learning, federated learning typically requires all edge devices to train a complete model locally. However, with the increasing scale of artificial intelligence models, the limited resources on edge devices often become a bottleneck for efficient fine-tuning. To address this challe... | {
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2412.06418 | Continual Learning for Segment Anything Model Adaptation | [
"cs.CV"
] | Although the current different types of SAM adaptation methods have achieved promising performance for various downstream tasks, such as prompt-based ones and adapter-based ones, most of them belong to the one-step adaptation paradigm. In real-world scenarios, we are generally confronted with the dynamic scenario where... | {
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2412.06419 | LLM-BIP: Structured Pruning for Large Language Models with Block-Wise
Forward Importance Propagation | [
"cs.CL",
"cs.AI"
] | Large language models (LLMs) have demonstrated remarkable performance across various language tasks, but their widespread deployment is impeded by their large size and high computational costs. Structural pruning is a prevailing technique used to introduce sparsity into pre-trained models and facilitate direct hardware... | {
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2412.06421 | Physical Layer Security in AmBC-NOMA Networks with Random Eavesdroppers | [
"eess.SY",
"cs.SY"
] | In this work, we investigate the physical layer security (PLS) of ambient backscatter communication non-orthogonal multiple access (AmBC-NOMA) networks where non-colluding eavesdroppers (Eves) are randomly distributed. In the proposed system, a base station (BS) transmits a superimposed signal to a typical NOMA user pa... | {
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2412.06424 | Deblur4DGS: 4D Gaussian Splatting from Blurry Monocular Video | [
"cs.CV"
] | Recent 4D reconstruction methods have yielded impressive results but rely on sharp videos as supervision. However, motion blur often occurs in videos due to camera shake and object movement, while existing methods render blurry results when using such videos for reconstructing 4D models. Although a few NeRF-based appro... | {
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2412.06425 | Foresee and Act Ahead: Task Prediction and Pre-Scheduling Enabled
Efficient Robotic Warehousing | [
"cs.RO",
"cs.MA"
] | In warehousing systems, to enhance logistical efficiency amid surging demand volumes, much focus is placed on how to reasonably allocate tasks to robots. However, the robots labor is still inevitably wasted to some extent. In response to this, we propose a pre-scheduling enhanced warehousing framework that predicts tas... | {
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2412.06432 | Integrating Expert Labels into LLM-based Emission Goal Detection:
Example Selection vs Automatic Prompt Design | [
"cs.LG",
"cs.CL"
] | We address the detection of emission reduction goals in corporate reports, an important task for monitoring companies' progress in addressing climate change. Specifically, we focus on the issue of integrating expert feedback in the form of labeled example passages into LLM-based pipelines, and compare the two strategie... | {
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2412.06435 | Simulating Human-like Daily Activities with Desire-driven Autonomy | [
"cs.AI"
] | Existing task-oriented AI agents often depend on explicit instructions or external rewards, limiting their ability to be driven by intrinsic motivations like humans. In this paper, we present a desire-driven autonomy framework to guide a Large Language Model-based (LLM-based) agent to simulate human-like daily activiti... | {
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2412.06436 | An Adaptively Inexact Method for Bilevel Learning Using Primal-Dual
Style Differentiation | [
"math.OC",
"cs.LG"
] | We consider a bilevel learning framework for learning linear operators. In this framework, the learnable parameters are optimized via a loss function that also depends on the minimizer of a convex optimization problem (denoted lower-level problem). We utilize an iterative algorithm called `piggyback' to compute the gra... | {
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2412.06438 | Can foundation models actively gather information in interactive
environments to test hypotheses? | [
"cs.LG",
"stat.ML"
] | While problem solving is a standard evaluation task for foundation models, a crucial component of problem solving -- actively and strategically gathering information to test hypotheses -- has not been closely investigated. To assess the information gathering abilities of foundation models in interactive environments, w... | {
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2412.06439 | Local Attention Transformers for High-Detail Optical Flow Upsampling | [
"cs.CV"
] | Most recent works on optical flow use convex upsampling as the last step to obtain high-resolution flow. In this work, we show and discuss several issues and limitations of this currently widely adopted convex upsampling approach. We propose a series of changes, in an attempt to resolve current issues. First, we propos... | {
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2412.06441 | BoRA: Bi-dimensional Weight-Decomposed Low-Rank Adaptation | [
"cs.CL"
] | In recent years, Parameter-Efficient Fine-Tuning (PEFT) methods like Low-Rank Adaptation (LoRA) have significantly enhanced the adaptability of large-scale pre-trained models. Weight-Decomposed Low-Rank Adaptation (DoRA) improves upon LoRA by separating the magnitude and direction components of the weight matrix, leadi... | {
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2412.06445 | Echocardiography to Cardiac MRI View Transformation for Real-Time Blind
Restoration | [
"eess.IV",
"cs.CV",
"cs.LG"
] | Echocardiography is the most widely used imaging to monitor cardiac functions, serving as the first line in early detection of myocardial ischemia and infarction. However, echocardiography often suffers from several artifacts including sensor noise, lack of contrast, severe saturation, and missing myocardial segments w... | {
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2412.06451 | How Certain are Uncertainty Estimates? Three Novel Earth Observation
Datasets for Benchmarking Uncertainty Quantification in Machine Learning | [
"cs.LG",
"cs.AI",
"eess.IV"
] | Uncertainty quantification (UQ) is essential for assessing the reliability of Earth observation (EO) products. However, the extensive use of machine learning models in EO introduces an additional layer of complexity, as those models themselves are inherently uncertain. While various UQ methods do exist for machine lear... | {
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2412.06454 | Adaptive Graph Learning from Spatial Information for Surgical Workflow
Anticipation | [
"cs.CV",
"cs.RO"
] | Surgical workflow anticipation is the task of predicting the timing of relevant surgical events from live video data, which is critical in Robotic-Assisted Surgery (RAS). Accurate predictions require the use of spatial information to model surgical interactions. However, current methods focus solely on surgical instrum... | {
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2412.06456 | UAV Virtual Antenna Array Deployment for Uplink Interference Mitigation
in Data Collection Networks | [
"cs.NE",
"cs.NI"
] | Unmanned aerial vehicles (UAVs) have gained considerable attention as a platform for establishing aerial wireless networks and communications. However, the line-of-sight dominance in air-to-ground communications often leads to significant interference with terrestrial networks, reducing communication efficiency among t... | {
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2412.06458 | Pruning All-Rounder: Rethinking and Improving Inference Efficiency for
Large Vision Language Models | [
"cs.CV"
] | Although Large Vision-Language Models (LVLMs) have achieved impressive results, their high computational cost poses a significant barrier to wider application. To enhance inference efficiency, most existing approaches depend on parameter-dependent or token-dependent strategies to reduce computational demands. However, ... | {
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2412.06461 | Ranked from Within: Ranking Large Multimodal Models for Visual Question
Answering Without Labels | [
"cs.CV"
] | As large multimodal models (LMMs) are increasingly deployed across diverse applications, the need for adaptable, real-world model ranking has become paramount. Traditional evaluation methods are largely dataset-centric, relying on fixed, labeled datasets and supervised metrics, which are resource-intensive and may lack... | {
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2412.06464 | Gated Delta Networks: Improving Mamba2 with Delta Rule | [
"cs.CL",
"cs.LG"
] | Linear Transformers have gained attention as efficient alternatives to standard Transformers, but their performance in retrieval and long-context tasks has been limited. To address these limitations, recent work has explored two distinct mechanisms: gating for adaptive memory control and the delta update rule for preci... | {
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2412.06465 | Agent Journey Beyond RGB: Unveiling Hybrid Semantic-Spatial
Environmental Representations for Vision-and-Language Navigation | [
"cs.CV",
"cs.MM"
] | Navigating unseen environments based on natural language instructions remains difficult for egocentric agents in Vision-and-Language Navigation (VLN). While recent advancements have yielded promising outcomes, they primarily rely on RGB images for environmental representation, often overlooking the underlying semantic ... | {
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2412.06468 | How many continuous measurements are needed to learn a vector? | [
"math.NA",
"cs.CC",
"cs.IT",
"cs.NA",
"math.IT"
] | One can recover vectors from $\mathbb{R}^m$ with arbitrary precision, using only $\lceil \log_2(m+1)\rceil +1$ continuous measurements that are chosen adaptively. This surprising result is explained and discussed, and we present applications to infinite-dimensional approximation problems. | {
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2412.06470 | Active Learning with Context Sampling and One-vs-Rest Entropy for
Semantic Segmentation | [
"cs.CV",
"cs.LG"
] | Multi-class semantic segmentation remains a cornerstone challenge in computer vision. Yet, dataset creation remains excessively demanding in time and effort, especially for specialized domains. Active Learning (AL) mitigates this challenge by selecting data points for annotation strategically. However, existing patch-b... | {
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2412.06472 | Food for thought: How can machine learning help better predict and
understand changes in food prices? | [
"cs.LG"
] | In this work, we address a lack of systematic understanding of fluctuations in food affordability in Canada. Canada's Food Price Report (CPFR) is an annual publication that predicts food inflation over the next calendar year. The published predictions are a collaborative effort between forecasting teams that each emplo... | {
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2412.06474 | From Uncertainty to Trust: Enhancing Reliability in Vision-Language
Models with Uncertainty-Guided Dropout Decoding | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Large vision-language models (LVLMs) demonstrate remarkable capabilities in multimodal tasks but are prone to misinterpreting visual inputs, often resulting in hallucinations and unreliable outputs. To address these challenges, we propose Dropout Decoding, a novel inference-time approach that quantifies the uncertainty... | {
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2412.06478 | An inferential measure of dependence between two systems using Bayesian
model comparison | [
"stat.ML",
"cs.LG",
"q-bio.QM"
] | We propose to quantify dependence between two systems $X$ and $Y$ in a dataset $D$ based on the Bayesian comparison of two models: one, $H_0$, of statistical independence and another one, $H_1$, of dependence. In this framework, dependence between $X$ and $Y$ in $D$, denoted $B(X,Y|D)$, is quantified as $P(H_1|D)$, the... | {
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2412.06482 | A Cardinality-Constrained Approach to Combinatorial Bilevel Congestion
Pricing | [
"math.OC",
"cs.GT",
"cs.SY",
"eess.SY"
] | Combinatorial bilevel congestion pricing (CBCP), a variant of the discrete network design problem, seeks to minimize the total travel time experienced by all travelers in a road network, by strategically selecting toll locations and determining the corresponding charges. Conventional wisdom suggests that these problems... | {
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2412.06483 | SafeWorld: Geo-Diverse Safety Alignment | [
"cs.CL",
"cs.AI"
] | In the rapidly evolving field of Large Language Models (LLMs), ensuring safety is a crucial and widely discussed topic. However, existing works often overlook the geo-diversity of cultural and legal standards across the world. To demonstrate the challenges posed by geo-diverse safety standards, we introduce SafeWorld, ... | {
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2412.06484 | Small Languages, Big Models: A Study of Continual Training on Languages
of Norway | [
"cs.CL"
] | Training large language models requires vast amounts of data, posing a challenge for less widely spoken languages like Norwegian and even more so for truly low-resource languages like Northern S\'ami. To address this issue, we present a novel three-stage continual training approach that substantially improves the downs... | {
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2412.06485 | Fourier-enhanced reduced-order surrogate modeling for uncertainty
quantification in electric machine design | [
"cs.CE"
] | This work proposes a data-driven surrogate modeling framework for cost-effectively inferring the torque of a permanent magnet synchronous machine under geometric design variations. The framework is separated into a reduced-order modeling and an inference part. Given a dataset of torque signals, each corresponding to a ... | {
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2412.06486 | SimuDICE: Offline Policy Optimization Through World Model Updates and
DICE Estimation | [
"cs.LG",
"cs.AI"
] | In offline reinforcement learning, deriving an effective policy from a pre-collected set of experiences is challenging due to the distribution mismatch between the target policy and the behavioral policy used to collect the data, as well as the limited sample size. Model-based reinforcement learning improves sample eff... | {
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2412.06487 | Improving text-conditioned latent diffusion for cancer pathology | [
"eess.IV",
"cs.CV",
"cs.LG"
] | The development of generative models in the past decade has allowed for hyperrealistic data synthesis. While potentially beneficial, this synthetic data generation process has been relatively underexplored in cancer histopathology. One algorithm for synthesising a realistic image is diffusion; it iteratively converts a... | {
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2412.06488 | An Efficient Scene Coordinate Encoding and Relocalization Method | [
"cs.RO",
"cs.CV"
] | Scene Coordinate Regression (SCR) is a visual localization technique that utilizes deep neural networks (DNN) to directly regress 2D-3D correspondences for camera pose estimation. However, current SCR methods often face challenges in handling repetitive textures and meaningless areas due to their reliance on implicit t... | {
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2412.06491 | PPT: Pre-Training with Pseudo-Labeled Trajectories for Motion
Forecasting | [
"cs.CV",
"cs.RO"
] | Motion forecasting (MF) for autonomous driving aims at anticipating trajectories of surrounding agents in complex urban scenarios. In this work, we investigate a mixed strategy in MF training that first pre-train motion forecasters on pseudo-labeled data, then fine-tune them on annotated data. To obtain pseudo-labeled ... | {
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2412.06494 | A cautionary tale on the cost-effectiveness of collaborative AI in
real-world medical applications | [
"cs.LG"
] | Background. Federated learning (FL) has gained wide popularity as a collaborative learning paradigm enabling collaborative AI in sensitive healthcare applications. Nevertheless, the practical implementation of FL presents technical and organizational challenges, as it generally requires complex communication infrastruc... | {
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2412.06497 | New Channel Coding Lower Bounds for Noisy Permutation Channels | [
"cs.IT",
"math.IT"
] | Motivated by the application of point-to-point communication networks and biological storage, we investigate new achievability bounds for noisy permutation channels with strictly positive and full-rank square matrices. Our new bounds use $\epsilon$-packing with Kullback-Leibler divergence as a metric to bound the dista... | {
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2412.06499 | HYATT-Net is Grand: A Hybrid Attention Network for Performant Anatomical
Landmark Detection | [
"cs.CV"
] | Anatomical landmark detection (ALD) from a medical image is crucial for a wide array of clinical applications. While existing methods achieve quite some success in ALD, they often struggle to balance global context with computational efficiency, particularly with high-resolution images, thereby leading to the rise of a... | {
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2412.06507 | BATseg: Boundary-aware Multiclass Spinal Cord Tumor Segmentation on 3D
MRI Scans | [
"eess.IV",
"cs.CV",
"cs.LG"
] | Spinal cord tumors significantly contribute to neurological morbidity and mortality. Precise morphometric quantification, encompassing the size, location, and type of such tumors, holds promise for optimizing treatment planning strategies. Although recent methods have demonstrated excellent performance in medical image... | {
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2412.06510 | AnomalyControl: Learning Cross-modal Semantic Features for Controllable
Anomaly Synthesis | [
"cs.CV",
"cs.AI"
] | Anomaly synthesis is a crucial approach to augment abnormal data for advancing anomaly inspection. Based on the knowledge from the large-scale pre-training, existing text-to-image anomaly synthesis methods predominantly focus on textual information or coarse-aligned visual features to guide the entire generation proces... | {
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2412.06511 | Fitting Spherical Gaussians to Dynamic HDRI Sequences | [
"cs.CV",
"cs.GR"
] | We present a technique for fitting high dynamic range illumination (HDRI) sequences using anisotropic spherical Gaussians (ASGs) while preserving temporal consistency in the compressed HDRI maps. Our approach begins with an optimization network that iteratively minimizes a composite loss function, which includes both r... | {
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2412.06512 | The Fusion of Large Language Models and Formal Methods for Trustworthy
AI Agents: A Roadmap | [
"cs.AI",
"cs.CL",
"cs.SE"
] | Large Language Models (LLMs) have emerged as a transformative AI paradigm, profoundly influencing daily life through their exceptional language understanding and contextual generation capabilities. Despite their remarkable performance, LLMs face a critical challenge: the propensity to produce unreliable outputs due to ... | {
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2412.06530 | HES-UNet: A U-Net for Hepatic Echinococcosis Lesion Segmentation | [
"eess.IV",
"cs.AI",
"cs.CV"
] | Hepatic echinococcosis (HE) is a prevalent disease in economically underdeveloped pastoral areas, where adequate medical resources are usually lacking. Existing methods often ignore multi-scale feature fusion or focus only on feature fusion between adjacent levels, which may lead to insufficient feature fusion. To addr... | {
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2412.06531 | Unraveling the Complexity of Memory in RL Agents: an Approach for
Classification and Evaluation | [
"cs.LG",
"cs.AI"
] | The incorporation of memory into agents is essential for numerous tasks within the domain of Reinforcement Learning (RL). In particular, memory is paramount for tasks that require the utilization of past information, adaptation to novel environments, and improved sample efficiency. However, the term ``memory'' encompas... | {
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2412.06534 | Inverting Visual Representations with Detection Transformers | [
"cs.CV",
"cs.AI",
"cs.LG",
"cs.NE"
] | Understanding the mechanisms underlying deep neural networks in computer vision remains a fundamental challenge. While many prior approaches have focused on visualizing intermediate representations within deep neural networks, particularly convolutional neural networks, these techniques have yet to be thoroughly explor... | {
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2412.06538 | Understanding Factual Recall in Transformers via Associative Memories | [
"cs.LG",
"cs.CL",
"cs.IT",
"math.IT",
"stat.ML"
] | Large language models have demonstrated an impressive ability to perform factual recall. Prior work has found that transformers trained on factual recall tasks can store information at a rate proportional to their parameter count. In our work, we show that shallow transformers can use a combination of associative memor... | {
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2412.06540 | Sloth: scaling laws for LLM skills to predict multi-benchmark
performance across families | [
"cs.LG",
"cs.AI",
"stat.ML"
] | Scaling laws for large language models (LLMs) predict model performance based on parameters like size and training data. However, differences in training configurations and data processing across model families lead to significant variations in benchmark performance, making it difficult for a single scaling law to gene... | {
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2412.06541 | Numerical Estimation of Spatial Distributions under Differential Privacy | [
"cs.DB"
] | Estimating spatial distributions is important in data analysis, such as traffic flow forecasting and epidemic prevention. To achieve accurate spatial distribution estimation, the analysis needs to collect sufficient user data. However, collecting data directly from individuals could compromise their privacy. Most previ... | {
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2412.06545 | On How Iterative Magnitude Pruning Discovers Local Receptive Fields in
Fully Connected Neural Networks | [
"cs.LG"
] | Since its use in the Lottery Ticket Hypothesis, iterative magnitude pruning (IMP) has become a popular method for extracting sparse subnetworks that can be trained to high performance. Despite this, the underlying nature of IMP's general success remains unclear. One possibility is that IMP is especially capable of extr... | {
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2412.06549 | Prediction of Occluded Pedestrians in Road Scenes using Human-like
Reasoning: Insights from the OccluRoads Dataset | [
"cs.CV",
"cs.LG"
] | Pedestrian detection is a critical task in autonomous driving, aimed at enhancing safety and reducing risks on the road. Over recent years, significant advancements have been made in improving detection performance. However, these achievements still fall short of human perception, particularly in cases involving occlud... | {
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2412.06555 | When Dimensionality Reduction Meets Graph (Drawing) Theory: Introducing
a Common Framework, Challenges and Opportunities | [
"cs.LG"
] | In the vast landscape of visualization research, Dimensionality Reduction (DR) and graph analysis are two popular subfields, often essential to most visual data analytics setups. DR aims to create representations to support neighborhood and similarity analysis on complex, large datasets. Graph analysis focuses on ident... | {
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2412.06559 | ProcessBench: Identifying Process Errors in Mathematical Reasoning | [
"cs.AI",
"cs.CL",
"cs.LG"
] | As language models regularly make mistakes when solving math problems, automated identification of errors in the reasoning process becomes increasingly significant for their scalable oversight. In this paper, we introduce ProcessBench for measuring the ability to identify erroneous steps in mathematical reasoning. It c... | {
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2412.06566 | DEX: Data Channel Extension for Efficient CNN Inference on Tiny AI
Accelerators | [
"cs.LG"
] | Tiny machine learning (TinyML) aims to run ML models on small devices and is increasingly favored for its enhanced privacy, reduced latency, and low cost. Recently, the advent of tiny AI accelerators has revolutionized the TinyML field by significantly enhancing hardware processing power. These accelerators, equipped w... | {
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2412.06568 | CONDEN-FI: Consistency and Diversity Learning-based Multi-View
Unsupervised Feature and In-stance Co-Selection | [
"cs.LG"
] | The objective of multi-view unsupervised feature and instance co-selection is to simultaneously iden-tify the most representative features and samples from multi-view unlabeled data, which aids in mit-igating the curse of dimensionality and reducing instance size to improve the performance of down-stream tasks. However... | {
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2412.06575 | Data Quality Enhancement on the Basis of Diversity with Large Language
Models for Text Classification: Uncovered, Difficult, and Noisy | [
"cs.CL"
] | In recent years, the use of large language models (LLMs) for text classification has attracted widespread attention. Despite this, the classification accuracy of LLMs has not yet universally surpassed that of smaller models. LLMs can enhance their performance in text classification through fine-tuning. However, existin... | {
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2412.06578 | MoViE: Mobile Diffusion for Video Editing | [
"cs.CV"
] | Recent progress in diffusion-based video editing has shown remarkable potential for practical applications. However, these methods remain prohibitively expensive and challenging to deploy on mobile devices. In this study, we introduce a series of optimizations that render mobile video editing feasible. Building upon th... | {
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} |
2412.06581 | EmoSpeech: A Corpus of Emotionally Rich and Contextually Detailed Speech
Annotations | [
"cs.SD",
"cs.AI",
"eess.AS"
] | Advances in text-to-speech (TTS) technology have significantly improved the quality of generated speech, closely matching the timbre and intonation of the target speaker. However, due to the inherent complexity of human emotional expression, the development of TTS systems capable of controlling subtle emotional differe... | {
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} |
2412.06590 | Bridging the Divide: Reconsidering Softmax and Linear Attention | [
"cs.CV"
] | Widely adopted in modern Vision Transformer designs, Softmax attention can effectively capture long-range visual information; however, it incurs excessive computational cost when dealing with high-resolution inputs. In contrast, linear attention naturally enjoys linear complexity and has great potential to scale up to ... | {
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} |
2412.06592 | PrEditor3D: Fast and Precise 3D Shape Editing | [
"cs.CV",
"cs.GR"
] | We propose a training-free approach to 3D editing that enables the editing of a single shape within a few minutes. The edited 3D mesh aligns well with the prompts, and remains identical for regions that are not intended to be altered. To this end, we first project the 3D object onto 4-view images and perform synchroniz... | {
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} |
2412.06593 | Anchoring Bias in Large Language Models: An Experimental Study | [
"cs.CL"
] | Large Language Models (LLMs) like GPT-4 and Gemini have significantly advanced artificial intelligence by enabling machines to generate and comprehend human-like text. Despite their impressive capabilities, LLMs are not immune to limitations, including various biases. While much research has explored demographic biases... | {
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} |
2412.06597 | Self-Interested Agents in Collaborative Machine Learning: An
Incentivized Adaptive Data-Centric Framework | [
"cs.LG"
] | We propose a framework for adaptive data-centric collaborative machine learning among self-interested agents, coordinated by an arbiter. Designed to handle the incremental nature of real-world data, the framework operates in an online manner: at each time step, the arbiter collects a batch of data from agents, trains a... | {
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} |
2412.06599 | A No-Reference Medical Image Quality Assessment Method Based on
Automated Distortion Recognition Technology: Application to Preprocessing in
MRI-guided Radiotherapy | [
"eess.IV",
"cs.CV",
"physics.med-ph"
] | Objective:To develop a no-reference image quality assessment method using automated distortion recognition to boost MRI-guided radiotherapy precision.Methods:We analyzed 106,000 MR images from 10 patients with liver metastasis,captured with the Elekta Unity MR-LINAC.Our No-Reference Quality Assessment Model includes:1)... | {
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} |
2412.06600 | Advancing Music Therapy: Integrating Eastern Five-Element Music Theory
and Western Techniques with AI in the Novel Five-Element Harmony System | [
"cs.HC",
"cs.AI",
"cs.CY"
] | In traditional medical practices, music therapy has proven effective in treating various psychological and physiological ailments. Particularly in Eastern traditions, the Five Elements Music Therapy (FEMT), rooted in traditional Chinese medicine, possesses profound cultural significance and unique therapeutic philosoph... | {
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} |
2412.06601 | A switching Kalman filter approach to online mitigation and correction
of sensor corruption for inertial navigation | [
"eess.SY",
"cs.RO",
"cs.SY"
] | This paper introduces a novel approach to detect and address faulty or corrupted external sensors in the context of inertial navigation by leveraging a switching Kalman Filter combined with parameter augmentation. Instead of discarding the corrupted data, the proposed method retains and processes it, running multiple o... | {
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} |
2412.06602 | Towards Controllable Speech Synthesis in the Era of Large Language
Models: A Survey | [
"cs.CL",
"cs.AI",
"cs.LG",
"cs.MM",
"cs.SD",
"eess.AS"
] | Text-to-speech (TTS), also known as speech synthesis, is a prominent research area that aims to generate natural-sounding human speech from text. Recently, with the increasing industrial demand, TTS technologies have evolved beyond synthesizing human-like speech to enabling controllable speech generation. This includes... | {
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} |
2412.06604 | VOPy: A Framework for Black-box Vector Optimization | [
"cs.LG",
"stat.AP",
"stat.ML"
] | We introduce VOPy, an open-source Python library designed to address black-box vector optimization, where multiple objectives must be optimized simultaneously with respect to a partial order induced by a convex cone. VOPy extends beyond traditional multi-objective optimization (MOO) tools by enabling flexible, cone-bas... | {
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} |
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