Split (2)

train · 433k rows

id stringlengths 9 16	title stringlengths 4 278	abstract stringlengths 3 4.08k	cs.HC bool 2 classes	cs.CE bool 2 classes	cs.SD bool 2 classes	cs.SI bool 2 classes	cs.AI bool 2 classes	cs.IR bool 2 classes	cs.LG bool 2 classes	cs.RO bool 2 classes	cs.CL bool 2 classes	cs.IT bool 2 classes	cs.SY bool 2 classes	cs.CV bool 2 classes	cs.CR bool 2 classes	cs.CY bool 2 classes	cs.MA bool 2 classes	cs.NE bool 2 classes	cs.DB bool 2 classes	Other bool 2 classes	__index_level_0__ int64 0 541k
1807.01246	The Concatenated Structure of Quasi-Abelian Codes	The decomposition of a quasi-abelian code into shorter linear codes over larger alphabets was given in (Jitman, Ling, (2015)), extending the analogous Chinese remainder decomposition of quasi-cyclic codes (Ling, Sol\'e, (2001)). We give a concatenated decomposition of quasi-abelian codes and show, as in the quasi-cycli...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	102,017
2206.13953	RAW-GNN: RAndom Walk Aggregation based Graph Neural Network	Graph-Convolution-based methods have been successfully applied to representation learning on homophily graphs where nodes with the same label or similar attributes tend to connect with one another. Due to the homophily assumption of Graph Convolutional Networks (GCNs) that these methods use, they are not suitable for h...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	305,130
2207.10026	Locality Guidance for Improving Vision Transformers on Tiny Datasets	While the Vision Transformer (VT) architecture is becoming trendy in computer vision, pure VT models perform poorly on tiny datasets. To address this issue, this paper proposes the locality guidance for improving the performance of VTs on tiny datasets. We first analyze that the local information, which is of great imp...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	309,113
2405.17746	Rethinking Pruning for Backdoor Mitigation: An Optimization Perspective	Deep Neural Networks (DNNs) are known to be vulnerable to backdoor attacks, posing concerning threats to their reliable deployment. Recent research reveals that backdoors can be erased from infected DNNs by pruning a specific group of neurons, while how to effectively identify and remove these backdoor-associated neuro...	false	false	false	false	true	false	true	false	false	false	false	false	true	false	false	false	false	false	458,093
2207.06154	On the Robustness of Bayesian Neural Networks to Adversarial Attacks	Vulnerability to adversarial attacks is one of the principal hurdles to the adoption of deep learning in safety-critical applications. Despite significant efforts, both practical and theoretical, training deep learning models robust to adversarial attacks is still an open problem. In this paper, we analyse the geometry...	false	false	false	false	true	false	true	false	false	false	false	false	true	false	false	false	false	false	307,784
2001.08844	Brain Tumor Classification Using Deep Learning Technique -- A Comparison between Cropped, Uncropped, and Segmented Lesion Images with Different Sizes	Deep Learning is the newest and the current trend of the machine learning field that paid a lot of the researchers' attention in the recent few years. As a proven powerful machine learning tool, deep learning was widely used in several applications for solving various complex problems that require extremely high accura...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	161,403
2005.07751	In Layman's Terms: Semi-Open Relation Extraction from Scientific Texts	Information Extraction (IE) from scientific texts can be used to guide readers to the central information in scientific documents. But narrow IE systems extract only a fraction of the information captured, and Open IE systems do not perform well on the long and complex sentences encountered in scientific texts. In this...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	177,367
2412.02674	Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models	Self-improvement is a mechanism in Large Language Model (LLM) pre-training, post-training and test-time inference. We explore a framework where the model verifies its own outputs, filters or reweights data based on this verification, and distills the filtered data. Despite several empirical successes, a fundamental und...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	513,633
2406.00256	Over-the-Air Collaborative Inference with Feature Differential Privacy	Collaborative inference in next-generation networks can enhance Artificial Intelligence (AI) applications, including autonomous driving, personal identification, and activity classification. This method involves a three-stage process: a) data acquisition through sensing, b) feature extraction, and c) feature encoding f...	false	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	459,766
2406.15893	Statistical Models of Top-$k$ Partial Orders	In many contexts involving ranked preferences, agents submit partial orders over available alternatives. Statistical models often treat these as marginal in the space of total orders, but this approach overlooks information contained in the list length itself. In this work, we introduce and taxonomize approaches for jo...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	466,910
2202.08536	Are There Exceptions to Goodhart's Law? On the Moral Justification of Fairness-Aware Machine Learning	Fairness-aware machine learning (fair-ml) techniques are algorithmic interventions designed to ensure that individuals who are affected by the predictions of a machine learning model are treated fairly. The problem is often posed as an optimization problem, where the objective is to achieve high predictive performance ...	false	false	false	false	true	false	true	false	false	false	false	false	false	true	false	false	false	false	280,917
2410.23603	Using Multimodal Deep Neural Networks to Disentangle Language from Visual Aesthetics	When we experience a visual stimulus as beautiful, how much of that experience derives from perceptual computations we cannot describe versus conceptual knowledge we can readily translate into natural language? Disentangling perception from language in visually-evoked affective and aesthetic experiences through behavio...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	504,099
2405.02497	Prediction techniques for dynamic imaging with online primal-dual methods	Online optimisation facilitates the solution of dynamic inverse problems, such as image stabilisation, fluid flow monitoring, and dynamic medical imaging. In this paper, we improve upon previous work on predictive online primal-dual methods on two fronts. Firstly, we provide a more concise analysis that symmetrises pre...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	451,784
2412.16524	LLaVA-SLT: Visual Language Tuning for Sign Language Translation	In the realm of Sign Language Translation (SLT), reliance on costly gloss-annotated datasets has posed a significant barrier. Recent advancements in gloss-free SLT methods have shown promise, yet they often largely lag behind gloss-based approaches in terms of translation accuracy. To narrow this performance gap, we in...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	519,568
2310.02254	Learning unitaries with quantum statistical queries	We propose several algorithms for learning unitary operators from quantum statistical queries (QSQs) with respect to their Choi-Jamiolkowski state. Quantum statistical queries capture the capabilities of a learner with limited quantum resources, which receives as input only noisy estimates of expected values of measure...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	396,770
2105.13881	CausCF: Causal Collaborative Filtering for RecommendationEffect Estimation	To improve user experience and profits of corporations, modern industrial recommender systems usually aim to select the items that are most likely to be interacted with (e.g., clicks and purchases). However, they overlook the fact that users may purchase the items even without recommendations. To select these effective...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	237,441
2402.08113	Addressing cognitive bias in medical language models	There is increasing interest in the application large language models (LLMs) to the medical field, in part because of their impressive performance on medical exam questions. While promising, exam questions do not reflect the complexity of real patient-doctor interactions. In reality, physicians' decisions are shaped by...	true	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	428,965
2005.00436	Bipartite Flat-Graph Network for Nested Named Entity Recognition	In this paper, we propose a novel bipartite flat-graph network (BiFlaG) for nested named entity recognition (NER), which contains two subgraph modules: a flat NER module for outermost entities and a graph module for all the entities located in inner layers. Bidirectional LSTM (BiLSTM) and graph convolutional network (G...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	175,234
2201.07092	K-nearest Multi-agent Deep Reinforcement Learning for Collaborative Tasks with a Variable Number of Agents	Traditionally, the performance of multi-agent deep reinforcement learning algorithms are demonstrated and validated in gaming environments where we often have a fixed number of agents. In many industrial applications, the number of available agents can change at any given day and even when the number of agents is known...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	true	false	false	false	275,927
2409.12431	FlexiTex: Enhancing Texture Generation with Visual Guidance	Recent texture generation methods achieve impressive results due to the powerful generative prior they leverage from large-scale text-to-image diffusion models. However, abstract textual prompts are limited in providing global textural or shape information, which results in the texture generation methods producing blur...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	489,587
2408.08035	An Advanced Deep Learning Based Three-Stream Hybrid Model for Dynamic Hand Gesture Recognition	In the modern context, hand gesture recognition has emerged as a focal point. This is due to its wide range of applications, which include comprehending sign language, factories, hands-free devices, and guiding robots. Many researchers have attempted to develop more effective techniques for recognizing these hand gestu...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	480,826
1204.1880	Scalable Frames	Tight frames can be characterized as those frames which possess optimal numerical stability properties. In this paper, we consider the question of modifying a general frame to generate a tight frame by rescaling its frame vectors; a process which can also be regarded as perfect preconditioning of a frame by a diagonal ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	15,362
1810.01127	Predicate learning in neural systems: Discovering latent generative structures	Humans learn complex latent structures from their environments (e.g., natural language, mathematics, music, social hierarchies). In cognitive science and cognitive neuroscience, models that infer higher-order structures from sensory or first-order representations have been proposed to account for the complexity and fle...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	109,337
2406.06967	Dual Thinking and Logical Processing -- Are Multi-modal Large Language Models Closing the Gap with Human Vision ?	The dual thinking framework considers fast, intuitive processing and slower, logical processing. The perception of dual thinking in vision requires images where inferences from intuitive and logical processing differ. We introduce an adversarial dataset to provide evidence for the dual thinking framework in human visio...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	462,842
2411.02410	Web-based Augmented Reality with Auto-Scaling and Real-Time Head Tracking towards Markerless Neurointerventional Preoperative Planning and Training of Head-mounted Robotic Needle Insertion	Neurosurgery requires exceptional precision and comprehensive preoperative planning to ensure optimal patient outcomes. Despite technological advancements, there remains a need for intuitive, accessible tools to enhance surgical preparation and medical education in this field. Traditional methods often lack the immersi...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	505,479
2404.01875	Satellite Federated Edge Learning: Architecture Design and Convergence Analysis	The proliferation of low-earth-orbit (LEO) satellite networks leads to the generation of vast volumes of remote sensing data which is traditionally transferred to the ground server for centralized processing, raising privacy and bandwidth concerns. Federated edge learning (FEEL), as a distributed machine learning appro...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	true	443,627
2001.08700	EventMapper: Detecting Real-World Physical Events Using Corroborative and Probabilistic Sources	The ubiquity of social media makes it a rich source for physical event detection, such as disasters, and as a potential resource for crisis management resource allocation. There have been some recent works on leveraging social media sources for retrospective, after-the-fact event detection of large events such as earth...	false	false	false	false	false	true	true	false	true	false	false	false	false	false	false	false	false	false	161,361
1710.08464	Interpretable Machine Learning for Privacy-Preserving Pervasive Systems	Our everyday interactions with pervasive systems generate traces that capture various aspects of human behavior and enable machine learning algorithms to extract latent information about users. In this paper, we propose a machine learning interpretability framework that enables users to understand how these generated t...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	83,081
2003.03506	Columnwise Element Selection for Computationally Efficient Nonnegative Coupled Matrix Tensor Factorization	Coupled Matrix Tensor Factorization (CMTF) facilitates the integration and analysis of multiple data sources and helps discover meaningful information. Nonnegative CMTF (N-CMTF) has been employed in many applications for identifying latent patterns, prediction, and recommendation. However, due to the added complexity w...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	167,248
2304.11073	OLISIA: a Cascade System for Spoken Dialogue State Tracking	Though Dialogue State Tracking (DST) is a core component of spoken dialogue systems, recent work on this task mostly deals with chat corpora, disregarding the discrepancies between spoken and written language.In this paper, we propose OLISIA, a cascade system which integrates an Automatic Speech Recognition (ASR) model...	false	false	true	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	359,663
2009.00803	Embedded Development Boards for Edge-AI: A Comprehensive Report	The use of Deep Learning and Machine Learning is becoming pervasive day by day which is opening doors to new opportunities in every aspect of technology. Its application Ranges from Health-care to Self-driving Cars, Home Automation to Smart-agriculture, and Industry 4.0. Traditionally the majority of the processing for...	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	194,145
2206.07351	RecBole 2.0: Towards a More Up-to-Date Recommendation Library	In order to support the study of recent advances in recommender systems, this paper presents an extended recommendation library consisting of eight packages for up-to-date topics and architectures. First of all, from a data perspective, we consider three important topics related to data issues (i.e., sparsity, bias and...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	302,710
2201.07525	Educational Timetabling: Problems, Benchmarks, and State-of-the-Art Results	We propose a survey of the research contributions on the field of Educational Timetabling with a specific focus on "standard" formulations and the corresponding benchmark instances. We identify six of such formulations and we discuss their features, pointing out their relevance and usability. Other available formulatio...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	276,054
1703.09902	Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation	This paper surveys the current state of the art in Natural Language Generation (NLG), defined as the task of generating text or speech from non-linguistic input. A survey of NLG is timely in view of the changes that the field has undergone over the past decade or so, especially in relation to new (usually data-driven) ...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	true	false	false	70,825
1010.2440	Enabling Data Discovery through Virtual Internet Repositories	Mercury is a federated metadata harvesting, search and retrieval tool based on both open source and software developed at Oak Ridge National Laboratory. It was originally developed for NASA, and the Mercury development consortium now includes funding from NASA, USGS, and DOE. A major new version of Mercury was develope...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	true	7,882
2304.02541	PWESuite: Phonetic Word Embeddings and Tasks They Facilitate	Mapping words into a fixed-dimensional vector space is the backbone of modern NLP. While most word embedding methods successfully encode semantic information, they overlook phonetic information that is crucial for many tasks. We develop three methods that use articulatory features to build phonetically informed word em...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	356,463
2405.17603	Towards Biomechanical Evaluation of a Transformative Additively Manufactured Flexible Pedicle Screw for Robotic Spinal Fixation	Vital for spinal fracture treatment, pedicle screw fixation is the gold standard for spinal fixation procedures. Nevertheless, due to the screw pullout and loosening issues, this surgery often fails to be effective for patients suffering from osteoporosis (i.e., having low bone mineral density). These failures can be a...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	458,018
2209.07268	AssembleRL: Learning to Assemble Furniture from Their Point Clouds	The rise of simulation environments has enabled learning-based approaches for assembly planning, which is otherwise a labor-intensive and daunting task. Assembling furniture is especially interesting since furniture are intricate and pose challenges for learning-based approaches. Surprisingly, humans can solve furnitur...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	317,685
2005.14713	Controlling Fairness and Bias in Dynamic Learning-to-Rank	Rankings are the primary interface through which many online platforms match users to items (e.g. news, products, music, video). In these two-sided markets, not only the users draw utility from the rankings, but the rankings also determine the utility (e.g. exposure, revenue) for the item providers (e.g. publishers, se...	false	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	179,338
1611.04692	The norm of the Fourier transform on compact or discrete abelian groups	We calculate the norm of the Fourier operator from $L^p(X)$ to $L^q(\hat{X})$ when $X$ is an infinite locally compact abelian group that is, furthermore, compact or discrete. This subsumes the sharp Hausdorff-Young inequality on such groups. In particular, we identify the region in $(p,q)$-space where the norm is infin...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	63,887
2212.09196	Emergent Analogical Reasoning in Large Language Models	The recent advent of large language models has reinvigorated debate over whether human cognitive capacities might emerge in such generic models given sufficient training data. Of particular interest is the ability of these models to reason about novel problems zero-shot, without any direct training. In human cognition,...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	337,028
2207.02487	fybrrChat: A Distributed Chat Application for Secure P2P Messaging	The growing demand for connecting with each other across the world has proved to be a boon to the growth of social media platforms. But when it comes to ensuring the privacy and security of the platform, the control is in hands of few monopolies. Some claim to provide a secure medium of communication but their exploita...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	306,534
2412.00404	Hard-Label Black-Box Attacks on 3D Point Clouds	With the maturity of depth sensors in various 3D safety-critical applications, 3D point cloud models have been shown to be vulnerable to adversarial attacks. Almost all existing 3D attackers simply follow the white-box or black-box setting to iteratively update coordinate perturbations based on back-propagated or estim...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	512,639
2111.05486	Uncoupled Bandit Learning towards Rationalizability: Benchmarks, Barriers, and Algorithms	Under the uncoupled learning setup, the last-iterate convergence guarantee towards Nash equilibrium is shown to be impossible in many games. This work studies the last-iterate convergence guarantee in general games toward rationalizability, a key solution concept in epistemic game theory that relaxes the stringent beli...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	true	265,809
1908.07062	Recurrent Neural Networks: An Embedded Computing Perspective	Recurrent Neural Networks (RNNs) are a class of machine learning algorithms used for applications with time-series and sequential data. Recently, there has been a strong interest in executing RNNs on embedded devices. However, difficulties have arisen because RNN requires high computational capability and a large memor...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	142,189
2102.01662	Private Linear Transformation: The Individual Privacy Case	This paper considers the single-server Private Linear Transformation (PLT) problem when individual privacy is required. In this problem, there is a user that wishes to obtain $L$ linear combinations of a $D$-subset of messages belonging to a dataset of $K$ messages stored on a single server. The goal is to minimize the...	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	218,186
2302.14004	Optimistic Planning by Regularized Dynamic Programming	We propose a new method for optimistic planning in infinite-horizon discounted Markov decision processes based on the idea of adding regularization to the updates of an otherwise standard approximate value iteration procedure. This technique allows us to avoid contraction and monotonicity arguments typically required b...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	348,121
2101.08779	AI Choreographer: Music Conditioned 3D Dance Generation with AIST++	We present AIST++, a new multi-modal dataset of 3D dance motion and music, along with FACT, a Full-Attention Cross-modal Transformer network for generating 3D dance motion conditioned on music. The proposed AIST++ dataset contains 5.2 hours of 3D dance motion in 1408 sequences, covering 10 dance genres with multi-view ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	216,415
2302.14340	HelixSurf: A Robust and Efficient Neural Implicit Surface Learning of Indoor Scenes with Iterative Intertwined Regularization	Recovery of an underlying scene geometry from multiview images stands as a long-time challenge in computer vision research. The recent promise leverages neural implicit surface learning and differentiable volume rendering, and achieves both the recovery of scene geometry and synthesis of novel views, where deep priors ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	348,255
2210.00673	Deep Learning for Wireless Networked Systems: a joint Estimation-Control-Scheduling Approach	Wireless networked control system (WNCS) connecting sensors, controllers, and actuators via wireless communications is a key enabling technology for highly scalable and low-cost deployment of control systems in the Industry 4.0 era. Despite the tight interaction of control and communications in WNCSs, most existing wor...	false	false	false	false	true	false	true	false	false	true	true	false	false	false	false	false	false	false	320,954
1602.08877	Design of PAR-Constrained Sequences for MIMO Channel Estimation via Majorization-Minimization	PAR-constrained sequences are widely used in communication systems and radars due to various practical needs; specifically, sequences are required to be unimodular or of low peak-to-average power ratio (PAR). For unimodular sequence design, plenty of efforts have been devoted to obtaining good correlation properties. R...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	52,703
2003.01769	Phonetic Feedback for Speech Enhancement With and Without Parallel Speech Data	While deep learning systems have gained significant ground in speech enhancement research, these systems have yet to make use of the full potential of deep learning systems to provide high-level feedback. In particular, phonetic feedback is rare in speech enhancement research even though it includes valuable top-down i...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	166,746
2302.11728	A Convolutional-Transformer Network for Crack Segmentation with Boundary Awareness	Cracks play a crucial role in assessing the safety and durability of manufactured buildings. However, the long and sharp topological features and complex background of cracks make the task of crack segmentation extremely challenging. In this paper, we propose a novel convolutional-transformer network based on encoder-d...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	347,295
1903.00534	Improved Differentially Private Analysis of Variance	Hypothesis testing is one of the most common types of data analysis and forms the backbone of scientific research in many disciplines. Analysis of variance (ANOVA) in particular is used to detect dependence between a categorical and a numerical variable. Here we show how one can carry out this hypothesis test under the...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	123,031
2112.15578	Importance of Empirical Sample Complexity Analysis for Offline Reinforcement Learning	We hypothesize that empirically studying the sample complexity of offline reinforcement learning (RL) is crucial for the practical applications of RL in the real world. Several recent works have demonstrated the ability to learn policies directly from offline data. In this work, we ask the question of the dependency on...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	273,827
1402.0147	A Probabilistic Method for Nonlinear Robustness Analysis of F-16 Controllers	This paper presents a new framework for controller robustness verification with respect to F-16 aircraft's closed-loop performance in longitudinal flight. We compare the state regulation performance of a linear quadratic regulator (LQR) and a gain-scheduled linear quadratic regulator (gsLQR), applied to nonlinear open-...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	30,539
2305.14843	Meta-learning For Vision-and-language Cross-lingual Transfer	Current pre-trained vison-language models (PVLMs) achieve excellent performance on a range of multi-modal datasets. Recent work has aimed at building multilingual models, and a range of novel multilingual multi-modal datasets have been proposed. Current PVLMs typically perform poorly on these datasets when used for mul...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	367,292
2406.08633	Unraveling Code-Mixing Patterns in Migration Discourse: Automated Detection and Analysis of Online Conversations on Reddit	The surge in global migration patterns underscores the imperative of integrating migrants seamlessly into host communities, necessitating inclusive and trustworthy public services. Despite the Nordic countries' robust public sector infrastructure, recent immigrants often encounter barriers to accessing these services, ...	true	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	463,558
1502.01418	RELEAF: An Algorithm for Learning and Exploiting Relevance	Recommender systems, medical diagnosis, network security, etc., require on-going learning and decision-making in real time. These -- and many others -- represent perfect examples of the opportunities and difficulties presented by Big Data: the available information often arrives from a variety of sources and has divers...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	39,927
1905.13456	Combining Noise-to-Image and Image-to-Image GANs: Brain MR Image Augmentation for Tumor Detection	Convolutional Neural Networks (CNNs) achieve excellent computer-assisted diagnosis with sufficient annotated training data. However, most medical imaging datasets are small and fragmented. In this context, Generative Adversarial Networks (GANs) can synthesize realistic/diverse additional training images to fill the dat...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	133,140
2406.05531	Enhancing Adversarial Transferability via Information Bottleneck Constraints	From the perspective of information bottleneck (IB) theory, we propose a novel framework for performing black-box transferable adversarial attacks named IBTA, which leverages advancements in invariant features. Intuitively, diminishing the reliance of adversarial perturbations on the original data, under equivalent att...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	462,172
2308.14178	Data-Driven Robust Control Using Prediction Error Bounds Based on Perturbation Analysis	For linear systems, many data-driven control methods rely on the behavioral framework, using historical data of the system to predict the future trajectories. However, measurement noise introduces errors in predictions. When the noise is bounded, we propose a method for designing historical experiments that enable the ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	388,222
2502.06469	Stochastic MPC with Online-optimized Policies and Closed-loop Guarantees	This paper proposes a stochastic model predictive control method for linear systems affected by additive Gaussian disturbances. Closed-loop satisfaction of probabilistic constraints and recursive feasibility of the underlying convex optimization problem is guaranteed. Optimization over feedback policies online increase...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	532,094
2306.02960	Best of Both Worlds: Hybrid SNN-ANN Architecture for Event-based Optical Flow Estimation	In the field of robotics, event-based cameras are emerging as a promising low-power alternative to traditional frame-based cameras for capturing high-speed motion and high dynamic range scenes. This is due to their sparse and asynchronous event outputs. Spiking Neural Networks (SNNs) with their asynchronous event-drive...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	371,132
1808.02299	Motorcycle detection and classification in urban Scenarios using a model based on Faster R-CNN	This paper introduces a Deep Learning Convolutional Neural Network model based on Faster-RCNN for motorcycle detection and classification on urban environments. The model is evaluated in occluded scenarios where more than 60% of the vehicles present a degree of occlusion. For training and evaluation, we introduce a new...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	104,748
1207.6788	Submartingale Property of E_0 Under The Polarization Transformations	We prove that the relation $E_0(\rho, W^{-}) + E_0(\rho, W^{+}) \geq 2 E_0(\rho, W)$ holds for any binary input discrete memoryless channel $W$, and $\rho \geq 0$.	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	17,813
2307.11993	Verifiable Sustainability in Data Centers	Data centers have significant energy needs, both embodied and operational, affecting sustainability adversely. The current techniques and tools for collecting, aggregating, and reporting verifiable sustainability data are vulnerable to cyberattacks and misuse, requiring new security and privacy-preserving solutions. Th...	false	false	false	false	false	false	false	false	false	false	true	false	true	true	false	false	false	true	381,104
2312.17205	EFHQ: Multi-purpose ExtremePose-Face-HQ dataset	The existing facial datasets, while having plentiful images at near frontal views, lack images with extreme head poses, leading to the downgraded performance of deep learning models when dealing with profile or pitched faces. This work aims to address this gap by introducing a novel dataset named Extreme Pose Face High...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	418,648
1205.2614	Products of Hidden Markov Models: It Takes N>1 to Tango	Products of Hidden Markov Models(PoHMMs) are an interesting class of generative models which have received little attention since their introduction. This maybe in part due to their more computationally expensive gradient-based learning algorithm,and the intractability of computing the log likelihood of sequences under...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	15,921
2411.00425	Cityscape-Adverse: Benchmarking Robustness of Semantic Segmentation with Realistic Scene Modifications via Diffusion-Based Image Editing	Recent advancements in generative AI, particularly diffusion-based image editing, have enabled the transformation of images into highly realistic scenes using only text instructions. This technology offers significant potential for generating diverse synthetic datasets to evaluate model robustness. In this paper, we in...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	504,593
1106.4058	Experimental Support for a Categorical Compositional Distributional Model of Meaning	Modelling compositional meaning for sentences using empirical distributional methods has been a challenge for computational linguists. We implement the abstract categorical model of Coecke et al. (arXiv:1003.4394v1 [cs.CL]) using data from the BNC and evaluate it. The implementation is based on unsupervised learning of...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	10,924
2409.05592	ExDDI: Explaining Drug-Drug Interaction Predictions with Natural Language	Predicting unknown drug-drug interactions (DDIs) is crucial for improving medication safety. Previous efforts in DDI prediction have typically focused on binary classification or predicting DDI categories, with the absence of explanatory insights that could enhance trust in these predictions. In this work, we propose t...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	486,830
2201.06837	Landslide Susceptibility Modeling by Interpretable Neural Network	Landslides are notoriously difficult to predict because numerous spatially and temporally varying factors contribute to slope stability. Artificial neural networks (ANN) have been shown to improve prediction accuracy but are largely uninterpretable. Here we introduce an additive ANN optimization framework to assess lan...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	275,850
2211.01860	Learning safety in model-based Reinforcement Learning using MPC and Gaussian Processes	We propose a method to encourage safety in Model Predictive Control (MPC)-based Reinforcement Learning (RL) via Gaussian Process (GP) regression. This framework consists of 1) a parametric MPC scheme that is employed as model-based controller with approximate knowledge on the real system's dynamics, 2) an episodic RL a...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	328,391
2405.16016	ComFace: Facial Representation Learning with Synthetic Data for Comparing Faces	Daily monitoring of intra-personal facial changes associated with health and emotional conditions has great potential to be useful for medical, healthcare, and emotion recognition fields. However, the approach for capturing intra-personal facial changes is relatively unexplored due to the difficulty of collecting tempo...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	457,212
1801.02687	Term Relevance Feedback for Contextual Named Entity Retrieval	We address the role of a user in Contextual Named Entity Retrieval (CNER), showing (1) that user identification of important context-bearing terms is superior to automated approaches, and (2) that further gains are possible if the user indicates the relative importance of those terms. CNER is similar in spirit to List ...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	87,964
2001.08682	Expected Information Maximization: Using the I-Projection for Mixture Density Estimation	Modelling highly multi-modal data is a challenging problem in machine learning. Most algorithms are based on maximizing the likelihood, which corresponds to the M(oment)-projection of the data distribution to the model distribution. The M-projection forces the model to average over modes it cannot represent. In contras...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	161,356
2004.10290	M-LVC: Multiple Frames Prediction for Learned Video Compression	We propose an end-to-end learned video compression scheme for low-latency scenarios. Previous methods are limited in using the previous one frame as reference. Our method introduces the usage of the previous multiple frames as references. In our scheme, the motion vector (MV) field is calculated between the current fra...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	173,595
1603.00567	MacroBase: Prioritizing Attention in Fast Data	As data volumes continue to rise, manual inspection is becoming increasingly untenable. In response, we present MacroBase, a data analytics engine that prioritizes end-user attention in high-volume fast data streams. MacroBase enables efficient, accurate, and modular analyses that highlight and aggregate important and ...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	52,788
2301.09580	Power Supply Compensation for Capacitive Loads	As ASIC supply voltages approach one volt, the source-impedance goals for power distribution networks are driven ever lower as well. One approach to achieving these goals is to add decoupling capacitors of various values until the desired impedance profile is obtained. An unintended consequence of this approach can be ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	341,541
cmp-lg/9405017	Best-first Model Merging for Hidden Markov Model Induction	This report describes a new technique for inducing the structure of Hidden Markov Models from data which is based on the general `model merging' strategy (Omohundro 1992). The process begins with a maximum likelihood HMM that directly encodes the training data. Successively more general models are produced by merging H...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	536,061
2410.23629	Posture-Informed Muscular Force Learning for Robust Hand Pressure Estimation	We present PiMForce, a novel framework that enhances hand pressure estimation by leveraging 3D hand posture information to augment forearm surface electromyography (sEMG) signals. Our approach utilizes detailed spatial information from 3D hand poses in conjunction with dynamic muscle activity from sEMG to enable accura...	true	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	504,112
2501.10395	Towards General Purpose Robots at Scale: Lifelong Learning and Learning to Use Memory	The widespread success of artificial intelligence in fields like natural language processing and computer vision has not yet fully transferred to robotics, where progress is hindered by the lack of large-scale training data and the complexity of real-world tasks. To address this, many robot learning researchers are pus...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	525,503
2105.10878	DepressionNet: A Novel Summarization Boosted Deep Framework for Depression Detection on Social Media	Twitter is currently a popular online social media platform which allows users to share their user-generated content. This publicly-generated user data is also crucial to healthcare technologies because the discovered patterns would hugely benefit them in several ways. One of the applications is in automatically discov...	false	false	false	true	false	false	true	false	true	false	false	false	false	false	false	false	false	false	236,526
2401.09476	A Framework for Agricultural Food Supply Chain using Blockchain	The main aim of the paper is to create a trust and transparency in the food supply chain system, ensuring food safety for everyone with the help of Blockchain Technology. Food supply chain is the process of tracing a crop from the farmer or producer to the buyer. With the advent of blockchain, providing a safe and frau...	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	false	422,279
2306.00180	FlowCam: Training Generalizable 3D Radiance Fields without Camera Poses via Pixel-Aligned Scene Flow	Reconstruction of 3D neural fields from posed images has emerged as a promising method for self-supervised representation learning. The key challenge preventing the deployment of these 3D scene learners on large-scale video data is their dependence on precise camera poses from structure-from-motion, which is prohibitiv...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	true	369,898
2206.01972	MACC: Cross-Layer Multi-Agent Congestion Control with Deep Reinforcement Learning	Congestion Control (CC), as the core networking task to efficiently utilize network capacity, received great attention and widely used in various Internet communication applications such as 5G, Internet-of-Things, UAN, and more. Various CC algorithms have been proposed both on network and transport layers such as Activ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	300,691
1801.10484	Cache-Aided Non-Orthogonal Multiple Access: The Two-User Case	In this paper, we propose a cache-aided non-orthogonal multiple access (NOMA) scheme for spectrally efficient downlink transmission. The proposed scheme not only reaps the benefits associated with NOMA and caching, but also exploits the data cached at the users for interference cancellation. As a consequence, caching c...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	89,299
2107.12178	Novel Span Measure, Spanning Sets and Applications	Rough Set based Spanning Sets were recently proposed to deal with uncertainties arising in the problem in domain of natural language processing problems. This paper presents a novel span measure using upper approximations. The key contribution of this paper is to propose another uncertainty measure of span and spanning...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	247,824
2407.01953	CatMemo at the FinLLM Challenge Task: Fine-Tuning Large Language Models using Data Fusion in Financial Applications	The integration of Large Language Models (LLMs) into financial analysis has garnered significant attention in the NLP community. This paper presents our solution to IJCAI-2024 FinLLM challenge, investigating the capabilities of LLMs within three critical areas of financial tasks: financial classification, financial tex...	false	true	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	469,525
1209.1719	Semi-metric networks for recommender systems	Weighted graphs obtained from co-occurrence in user-item relations lead to non-metric topologies. We use this semi-metric behavior to issue recommendations, and discuss its relationship to transitive closure on fuzzy graphs. Finally, we test the performance of this method against other item- and user-based recommender ...	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	false	18,462
1403.4540	Similarity networks for classification: a case study in the Horse Colic problem	This paper develops a two-layer neural network in which the neuron model computes a user-defined similarity function between inputs and weights. The neuron transfer function is formed by composition of an adapted logistic function with the mean of the partial input-weight similarities. The resulting neuron model is cap...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	31,657
2308.14103	Towards Unified Token Learning for Vision-Language Tracking	In this paper, we present a simple, flexible and effective vision-language (VL) tracking pipeline, termed \textbf{MMTrack}, which casts VL tracking as a token generation task. Traditional paradigms address VL tracking task indirectly with sophisticated prior designs, making them over-specialize on the features of speci...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	388,191
0804.3361	A New Approach to Automated Epileptic Diagnosis Using EEG and Probabilistic Neural Network	Epilepsy is one of the most common neurological disorders that greatly impair patient' daily lives. Traditional epileptic diagnosis relies on tedious visual screening by neurologists from lengthy EEG recording that requires the presence of seizure (ictal) activities. Nowadays, there are many systems helping the neurolo...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	1,611
2008.08944	Localizing Anomalies from Weakly-Labeled Videos	Video anomaly detection under video-level labels is currently a challenging task. Previous works have made progresses on discriminating whether a video sequencecontains anomalies. However, most of them fail to accurately localize the anomalous events within videos in the temporal domain. In this paper, we propose a Wea...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	192,558
2501.18538	Mini-ResEmoteNet: Leveraging Knowledge Distillation for Human-Centered Design	Facial Emotion Recognition has emerged as increasingly pivotal in the domain of User Experience, notably within modern usability testing, as it facilitates a deeper comprehension of user satisfaction and engagement. This study aims to extend the ResEmoteNet model by employing a knowledge distillation framework to devel...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	528,745
1810.12126	ActionXPose: A Novel 2D Multi-view Pose-based Algorithm for Real-time Human Action Recognition	We present ActionXPose, a novel 2D pose-based algorithm for posture-level Human Action Recognition (HAR). The proposed approach exploits 2D human poses provided by OpenPose detector from RGB videos. ActionXPose aims to process poses data to be provided to a Long Short-Term Memory Neural Network and to a 1D Convolutiona...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	111,689
2407.12282	Chip Placement with Diffusion	Macro placement is a vital step in digital circuit design that defines the physical location of large collections of components, known as macros, on a 2-dimensional chip. The physical layout obtained during placement determines key performance metrics of the chip, such as power consumption, area, and performance. Exist...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	473,851
2304.01659	Diba: A Re-configurable Stream Processor	Stream processing acceleration is driven by the continuously increasing volume and velocity of data generated on the Web and the limitations of storage, computation, and power consumption. Hardware solutions provide better performance and power consumption, but they are hindered by the high research and development cos...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	356,166
2110.00242	3rd Place Scheme on Instance Segmentation Track of ICCV 2021 VIPriors Challenges	In this paper, we introduce a data-efficient instance segmentation method we used in the 2021 VIPriors Instance Segmentation Challenge. Our solution is a modified version of Swin Transformer, based on the mmdetection which is a powerful toolbox. To solve the problem of lack of data, we utilize data augmentation includi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	258,329

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