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
2306.11816	Learning to Generate Better Than Your LLM	Reinforcement learning (RL) has emerged as a powerful paradigm for fine-tuning Large Language Models (LLMs) for text generation. In particular, recent LLMs such as ChatGPT and GPT-4 can engage in fluent conversations with users after finetuning with RL. Capitalizing on key properties of text generation, we seek to inve...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	374,718
2308.08753	BOTT: Box Only Transformer Tracker for 3D Object Tracking	Tracking 3D objects is an important task in autonomous driving. Classical Kalman Filtering based methods are still the most popular solutions. However, these methods require handcrafted designs in motion modeling and can not benefit from the growing data amounts. In this paper, Box Only Transformer Tracker (BOTT) is pr...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	386,018
2401.01301	Large Legal Fictions: Profiling Legal Hallucinations in Large Language Models	Do large language models (LLMs) know the law? These models are increasingly being used to augment legal practice, education, and research, yet their revolutionary potential is threatened by the presence of hallucinations -- textual output that is not consistent with legal facts. We present the first systematic evidence...	false	false	false	false	true	false	false	false	true	false	false	false	false	true	false	false	false	false	419,306
1811.01549	StNet: Local and Global Spatial-Temporal Modeling for Action Recognition	Despite the success of deep learning for static image understanding, it remains unclear what are the most effective network architectures for the spatial-temporal modeling in videos. In this paper, in contrast to the existing CNN+RNN or pure 3D convolution based approaches, we explore a novel spatial temporal network (...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	112,399
2404.14215	Text-Tuple-Table: Towards Information Integration in Text-to-Table Generation via Global Tuple Extraction	The task of condensing large chunks of textual information into concise and structured tables has gained attention recently due to the emergence of Large Language Models (LLMs) and their potential benefit for downstream tasks, such as text summarization and text mining. Previous approaches often generate tables that di...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	448,604
2501.09609	Adversarial-Ensemble Kolmogorov Arnold Networks for Enhancing Indoor Wi-Fi Positioning: A Defensive Approach Against Spoofing and Signal Manipulation Attacks	The research presents a study on enhancing the robustness of Wi-Fi-based indoor positioning systems against adversarial attacks. The goal is to improve the positioning accuracy and resilience of these systems under two attack scenarios: Wi-Fi Spoofing and Signal Strength Manipulation. Three models are developed and eva...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	525,201
2406.13175	Sparse High Rank Adapters	Low Rank Adaptation (LoRA) has gained massive attention in the recent generative AI research. One of the main advantages of LoRA is its ability to be fused with pretrained models, adding no overhead during inference. However, from a mobile deployment standpoint, we can either avoid inference overhead in the fused mode ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	465,732
2307.10947	Improving Online Lane Graph Extraction by Object-Lane Clustering	Autonomous driving requires accurate local scene understanding information. To this end, autonomous agents deploy object detection and online BEV lane graph extraction methods as a part of their perception stack. In this work, we propose an architecture and loss formulation to improve the accuracy of local lane graph e...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	380,749
1703.06708	Complex Number Formulation and Convex Relaxations for Aircraft Conflict Resolution	We present a novel complex number formulation along with tight convex relaxations for the aircraft conflict resolution problem. Our approach combines both speed and heading control and provides global optimality guarantees despite non-convexities in the feasible region. As a side result, we present a new characterizati...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	70,270
2101.04350	Automated Detection of Patellofemoral Osteoarthritis from Knee Lateral View Radiographs Using Deep Learning: Data from the Multicenter Osteoarthritis Study (MOST)	Objective: To assess the ability of imaging-based deep learning to predict radiographic patellofemoral osteoarthritis (PFOA) from knee lateral view radiographs. Design: Knee lateral view radiographs were extracted from The Multicenter Osteoarthritis Study (MOST) (n = 18,436 knees). Patellar region-of-interest (ROI) w...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	215,129
2008.00335	V2I Connectivity-Based Dynamic Queue-Jump Lane for Emergency Vehicles: A Deep Reinforcement Learning Approach	Emergency vehicle (EMV) service is a key function of cities and is exceedingly challenging due to urban traffic congestion. A main reason behind EMV service delay is the lack of communication and cooperation between vehicles blocking EMVs. In this paper, we study the improvement of EMV service under V2I connectivity. W...	false	false	false	false	true	false	true	false	false	false	true	false	false	false	false	false	false	false	189,982
2104.07663	Tourist route optimization in the context of Covid-19 pandemic	The paper presents an innovative method for tourist route planning inside a destination. The necessity of reorganizing the tourist routes within a destination comes as an immediate response to the Covid-19 crisis. The implementation of the method inside tourist destinations can be an important advantage in transforming...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	230,505
2104.10818	XAI-N: Sensor-based Robot Navigation using Expert Policies and Decision Trees	We present a novel sensor-based learning navigation algorithm to compute a collision-free trajectory for a robot in dense and dynamic environments with moving obstacles or targets. Our approach uses deep reinforcement learning-based expert policy that is trained using a sim2real paradigm. In order to increase the relia...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	231,722
1805.05518	Formal Modelling of Ontologies : An Event-B based Approach Using the Rodin Platform	This paper reports on the results of the French ANR IMPEX research project dealing with making explicit domain knowledge in design models. Ontologies are formalised as theories with sets, axioms, theorems and reasoning rules. They are integrated to design models through an annotation mechanism. Event-B has been chosen ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	97,441
2306.00262	Maximal Domain Independent Representations Improve Transfer Learning	The most effective domain adaptation (DA) involves the decomposition of data representation into a domain independent representation (DIRep), and a domain dependent representation (DDRep). A classifier is trained by using the DIRep of the labeled source images. Since the DIRep is domain invariant, the classifier can be...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	369,935
1508.02405	Gait Assessment for Multiple Sclerosis Patients Using Microsoft Kinect	Gait analysis of patients with neurological disorders, including multiple sclerosis (MS), is important for rehabilitation and treatment. The Mircrosoft Kinect sensor, which was developed for motion recognition in gaming applications, is an ideal candidate for an inexpensive system providing the capability for human gai...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	45,898
2405.20671	Position Coupling: Improving Length Generalization of Arithmetic Transformers Using Task Structure	Even for simple arithmetic tasks like integer addition, it is challenging for Transformers to generalize to longer sequences than those encountered during training. To tackle this problem, we propose position coupling, a simple yet effective method that directly embeds the structure of the tasks into the positional enc...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	459,462
2502.00168	Supervised Quadratic Feature Analysis: An Information Geometry Approach to Dimensionality Reduction	Supervised dimensionality reduction aims to map labeled data to a low-dimensional feature space while maximizing class discriminability. Despite the availability of methods for learning complex non-linear features (e.g. Deep Learning), there is an enduring demand for dimensionality reduction methods that learn linear f...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	529,236
2403.00554	Distributed MPC for autonomous ships on inland waterways with collaborative collision avoidance	This paper presents a distributed solution for the problem of collaborative collision avoidance for autonomous inland waterway ships. A two-layer collision avoidance framework that considers inland waterway traffic regulations is proposed to increase navigational safety for autonomous ships. Our approach allows for mod...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	434,021
2111.06537	Multi-Step Budgeted Bayesian Optimization with Unknown Evaluation Costs	Bayesian optimization (BO) is a sample-efficient approach to optimizing costly-to-evaluate black-box functions. Most BO methods ignore how evaluation costs may vary over the optimization domain. However, these costs can be highly heterogeneous and are often unknown in advance. This occurs in many practical settings, su...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	266,103
2007.05163	Handling Collocations in Hierarchical Latent Tree Analysis for Topic Modeling	Topic modeling has been one of the most active research areas in machine learning in recent years. Hierarchical latent tree analysis (HLTA) has been recently proposed for hierarchical topic modeling and has shown superior performance over state-of-the-art methods. However, the models used in HLTA have a tree structure ...	false	false	false	false	false	true	true	false	true	false	false	false	false	false	false	false	false	false	186,585
2108.10378	Lightweight Multi-person Total Motion Capture Using Sparse Multi-view Cameras	Multi-person total motion capture is extremely challenging when it comes to handle severe occlusions, different reconstruction granularities from body to face and hands, drastically changing observation scales and fast body movements. To overcome these challenges above, we contribute a lightweight total motion capture ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	251,876
2201.02718	Multi-Vehicle Control in Roundabouts using Decentralized Game-Theoretic Planning	Safe navigation in dense, urban driving environments remains an open problem and an active area of research. Unlike typical predict-then-plan approaches, game-theoretic planning considers how one vehicle's plan will affect the actions of another. Recent work has demonstrated significant improvements in the time require...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	274,623
1510.00083	Optimizing Energy Storage Participation in Emerging Power Markets	The growing amount of intermittent renewables in power generation creates challenges for real-time matching of supply and demand in the power grid. Emerging ancillary power markets provide new incentives to consumers (e.g., electrical vehicles, data centers, and others) to perform demand response to help stabilize the ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	47,485
1705.03366	Frequency Switching for Simultaneous Wireless Information and Power Transfer	A new frequency switching receiver structure is proposed for simultaneous wireless information and power transfer in multi-carrier communication systems. Each subcarrier is switched to either the energy harvesting unit or the information decoding unit, according to the optimal subcarrier allocation. To implement the sy...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	73,170
1810.06729	Robust Neural Machine Translation with Joint Textual and Phonetic Embedding	Neural machine translation (NMT) is notoriously sensitive to noises, but noises are almost inevitable in practice. One special kind of noise is the homophone noise, where words are replaced by other words with similar pronunciations. We propose to improve the robustness of NMT to homophone noises by 1) jointly embeddin...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	110,487
2006.03206	Achieving High Throughput and Elasticity in a Larger-than-Memory Store	Millions of sensors, mobile applications and machines now generate billions of events. Specialized many-core key-value stores (KVSs) can ingest and index these events at high rates (over 100 Mops/s on one machine) if events are generated on the same machine; however, to be practical and cost-effective they must ingest ...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	180,245
1401.0734	Repairable Fountain Codes	We introduce a new family of Fountain codes that are systematic and also have sparse parities. Given an input of $k$ symbols, our codes produce an unbounded number of output symbols, generating each parity independently by linearly combining a logarithmic number of randomly selected input symbols. The construction guar...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	29,583
2407.11089	Explainable bank failure prediction models: Counterfactual explanations to reduce the failure risk	The accuracy and understandability of bank failure prediction models are crucial. While interpretable models like logistic regression are favored for their explainability, complex models such as random forest, support vector machines, and deep learning offer higher predictive performance but lower explainability. These...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	473,301
2009.09266	Humans learn too: Better Human-AI Interaction using Optimized Human Inputs	Humans rely more and more on systems with AI components. The AI community typically treats human inputs as a given and optimizes AI models only. This thinking is one-sided and it neglects the fact that humans can learn, too. In this work, human inputs are optimized for better interaction with an AI model while keeping ...	true	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	196,518
1605.04951	Viziometrics: Analyzing Visual Information in the Scientific Literature	Scientific results are communicated visually in the literature through diagrams, visualizations, and photographs. These information-dense objects have been largely ignored in bibliometrics and scientometrics studies when compared to citations and text. In this paper, we use techniques from computer vision and machine l...	false	false	false	true	false	true	false	false	false	false	false	true	false	false	false	false	false	true	55,932
2112.01901	The Box Size Confidence Bias Harms Your Object Detector	Countless applications depend on accurate predictions with reliable confidence estimates from modern object detectors. It is well known, however, that neural networks including object detectors produce miscalibrated confidence estimates. Recent work even suggests that detectors' confidence predictions are biased with r...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	269,664
2305.00163	Enhancing Video Super-Resolution via Implicit Resampling-based Alignment	In video super-resolution, it is common to use a frame-wise alignment to support the propagation of information over time. The role of alignment is well-studied for low-level enhancement in video, but existing works overlook a critical step -- resampling. We show through extensive experiments that for alignment to be e...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	361,235
2311.06954	Multimodal Learning of Soft Robot Dynamics using Differentiable Filters	Differentiable Filters, as recursive Bayesian estimators, possess the ability to learn complex dynamics by deriving state transition and measurement models exclusively from data. This data-driven approach eliminates the reliance on explicit analytical models while maintaining the essential algorithmic components of the...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	407,138
2006.07834	Multi-Miner: Object-Adaptive Region Mining for Weakly-Supervised Semantic Segmentation	Object region mining is a critical step for weakly-supervised semantic segmentation. Most recent methods mine the object regions by expanding the seed regions localized by class activation maps. They generally do not consider the sizes of objects and apply a monotonous procedure to mining all the object regions. Thus t...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	181,967
2308.04659	A hyper-distance-based method for hypernetwork comparison	Hypernetwork is a useful way to depict multiple connections between nodes, making it an ideal tool for representing complex relationships in network science. In recent years, there has been a marked increase in studies on hypernetworks, however, the comparison of the difference between two hypernetworks has been given ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	384,496
1912.09025	Matrix-Calibration-Based Cascaded Channel Estimation for Reconfigurable Intelligent Surface Assisted Multiuser MIMO	Reconfigurable intelligent surface (RIS) is envisioned to be an essential component of the paradigm for beyond 5G networks as it can potentially provide similar or higher array gains with much lower hardware cost and energy consumption compared with the massive multiple-input multiple-output (MIMO) technology. In this ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	157,989
2202.13203	Dropout can Simulate Exponential Number of Models for Sample Selection Techniques	Following Coteaching, generally in the literature, two models are used in sample selection based approaches for training with noisy labels. Meanwhile, it is also well known that Dropout when present in a network trains an ensemble of sub-networks. We show how to leverage this property of Dropout to train an exponential...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	282,526
1408.0259	Permutation Trellis Coded Multi-level FSK Signaling to Mitigate Primary User Interference in Cognitive Radio Networks	We employ Permutation Trellis Code (PTC) based multi-level Frequency Shift Keying signaling to mitigate the impact of Primary Users (PUs) on the performance of Secondary Users (SUs) in Cognitive Radio Networks (CRNs). The PUs are assumed to be dynamic in that they appear intermittently and stay active for an unknown du...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	35,073
2311.16513	Fine-grained Appearance Transfer with Diffusion Models	Image-to-image translation (I2I), and particularly its subfield of appearance transfer, which seeks to alter the visual appearance between images while maintaining structural coherence, presents formidable challenges. Despite significant advancements brought by diffusion models, achieving fine-grained transfer remains ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	410,936
2010.12247	An Asymptotically Optimal Primal-Dual Incremental Algorithm for Contextual Linear Bandits	In the contextual linear bandit setting, algorithms built on the optimism principle fail to exploit the structure of the problem and have been shown to be asymptotically suboptimal. In this paper, we follow recent approaches of deriving asymptotically optimal algorithms from problem-dependent regret lower bounds and we...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	202,623
1911.08554	Classification as Decoder: Trading Flexibility for Control in Medical Dialogue	Generative seq2seq dialogue systems are trained to predict the next word in dialogues that have already occurred. They can learn from large unlabeled conversation datasets, build a deeper understanding of conversational context, and generate a wide variety of responses. This flexibility comes at the cost of control, a ...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	154,217
2406.10229	Quantifying Variance in Evaluation Benchmarks	Evaluation benchmarks are the cornerstone of measuring capabilities of large language models (LLMs), as well as driving progress in said capabilities. Originally designed to make claims about capabilities (or lack thereof) in fully pretrained models, evaluation benchmarks are now also extensively used to decide between...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	464,290
2501.12706	REX: Causal Discovery based on Machine Learning and Explainability techniques	Explainability techniques hold significant potential for enhancing the causal discovery process, which is crucial for understanding complex systems in areas like healthcare, economics, and artificial intelligence. However, no causal discovery methods currently incorporate explainability into their models to derive caus...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	526,411
2304.00201	Precoder Design for Massive MIMO Downlink with Matrix Manifold Optimization	We investigate the weighted sum-rate (WSR) maximization linear precoder design for massive multiple-input multiple-output (MIMO) downlink. We consider a single-cell system with multiple users and propose a unified matrix manifold optimization framework applicable to total power constraint (TPC), per-user power constrai...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	355,594
2410.17439	Evaluating AI-Generated Essays with GRE Analytical Writing Assessment	The recent revolutionary advance in generative AI enables the generation of realistic and coherent texts by large language models (LLMs). Despite many existing evaluation metrics on the quality of the generated texts, there is still a lack of rigorous assessment of how well LLMs perform in complex and demanding writing...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	501,459
2208.14876	NestedFormer: Nested Modality-Aware Transformer for Brain Tumor Segmentation	Multi-modal MR imaging is routinely used in clinical practice to diagnose and investigate brain tumors by providing rich complementary information. Previous multi-modal MRI segmentation methods usually perform modal fusion by concatenating multi-modal MRIs at an early/middle stage of the network, which hardly explores ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	315,444
2412.17916	Data-Driven Priors in the Maximum Entropy on the Mean Method for Linear Inverse Problems	We establish the theoretical framework for implementing the maximumn entropy on the mean (MEM) method for linear inverse problems in the setting of approximate (data-driven) priors. We prove a.s. convergence for empirical means and further develop general estimates for the difference between the MEM solutions with diff...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	520,171
2009.14794	Rethinking Attention with Performers	We introduce Performers, Transformer architectures which can estimate regular (softmax) full-rank-attention Transformers with provable accuracy, but using only linear (as opposed to quadratic) space and time complexity, without relying on any priors such as sparsity or low-rankness. To approximate softmax attention-ker...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	198,143
2111.12055	Generating GPU Compiler Heuristics using Reinforcement Learning	GPU compilers are complex software programs with many optimizations specific to target hardware. These optimizations are often controlled by heuristics hand-designed by compiler experts using time- and resource-intensive processes. In this paper, we developed a GPU compiler autotuning framework that uses off-policy dee...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	267,852
1902.06450	Self-Attention Aligner: A Latency-Control End-to-End Model for ASR Using Self-Attention Network and Chunk-Hopping	Self-attention network, an attention-based feedforward neural network, has recently shown the potential to replace recurrent neural networks (RNNs) in a variety of NLP tasks. However, it is not clear if the self-attention network could be a good alternative of RNNs in automatic speech recognition (ASR), which processes...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	121,769
2401.01579	An Invariant Information Geometric Method for High-Dimensional Online Optimization	Sample efficiency is crucial in optimization, particularly in black-box scenarios characterized by expensive evaluations and zeroth-order feedback. When computing resources are plentiful, Bayesian optimization is often favored over evolution strategies. In this paper, we introduce a full invariance oriented evolution s...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	419,418
1710.04979	Fundamental Limitations in Performance and Interpretability of Common Planar Rigid-Body Contact Models	The ability to reason about and predict the outcome of contacts is paramount to the successful execution of many robot tasks. Analytical rigid-body contact models are used extensively in planning and control due to their computational efficiency and simplicity, yet despite their prevalence, little if any empirical comp...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	82,560
2111.10899	Identification of Low Rank Vector Processes	We study modeling and identification of stationary processes with a spectral density matrix of low rank. Equivalently, we consider processes having an innovation of reduced dimension for which Prediction Error Methods (PEM) algorithms are not directly applicable. We show that these processes admit a special feedback st...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	267,483
2412.06461	Ranked from Within: Ranking Large Multimodal Models for Visual Question Answering Without Labels	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...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	515,253
1308.1162	Increasing Knowledge Worker Efficiency through a "Virtual Mirror" of the Social Network	In this paper we introduce a case study describing the combination of manual survey-based and e-mail-based social network analysis. The goal of the project was to increase collaboration efficiency in a team of consultants of a major high tech manufacturer. By analyzing the social network of a team of 42 consultants and...	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	false	26,282
1511.04919	Tales told by coloured tangles	Tangle machines are a topologically inspired diagrammatic formalism to describe information flow in networks. This paper begins with an expository account of tangle machines motivated by the problem of describing `covariance intersection' fusion of Gaussian estimators in networks. It then gives two examples in which ta...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	48,968
1911.07967	DLBricks: Composable Benchmark Generation to Reduce Deep Learning Benchmarking Effort on CPUs (Extended)	The past few years have seen a surge of applying Deep Learning (DL) models for a wide array of tasks such as image classification, object detection, machine translation, etc. While DL models provide an opportunity to solve otherwise intractable tasks, their adoption relies on them being optimized to meet latency and re...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	154,033
1805.06298	SAVERS: SAR ATR with Verification Support Based on Convolutional Neural Network	We propose a new convolutional neural network (CNN) which performs coarse and fine segmentation for end-to-end synthetic aperture radar (SAR) automatic target recognition (ATR) system. In recent years, many CNNs for SAR ATR using deep learning have been proposed, but most of them classify target classes from fixed size...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	97,577
2211.06973	A Variable Node Design with Check Node Aware Quantization Leveraging 2-Bit LDPC Decoding	For improving coarsely quantized decoding of LDPC codes, we propose a check node aware design of the variable node update. In contrast to previous works, we optimize the variable node to explicitly maximize the mutual information preserved in the check-to-variable instead of the variable-to-check node messages. The ext...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	330,085
1810.01345	NimbRo Rescue: Solving Disaster-Response Tasks through Mobile Manipulation Robot Momaro	Robots that solve complex tasks in environments too dangerous for humans to enter are desperately needed, e.g. for search and rescue applications. We describe our mobile manipulation robot Momaro, with which we participated successfully in the DARPA Robotics Challenge. It features a unique locomotion design with four l...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	109,374
1504.04122	Detecting Topology Variations in Dynamical Networks	This paper considers the problem of detecting topology variations in dynamical networks. We consider a network whose behavior can be represented via a linear dynamical system. The problem of interest is then that of finding conditions under which it is possible to detect node or link disconnections from prior knowledge...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	42,108
2107.11007	Dynamic Proximal Unrolling Network for Compressive Imaging	Compressive imaging aims to recover a latent image from under-sampled measurements, suffering from a serious ill-posed inverse problem. Recently, deep neural networks have been applied to this problem with superior results, owing to the learned advanced image priors. These approaches, however, require training separate...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	247,467
2002.11948	Features for Ground Texture Based Localization -- A Survey	Ground texture based vehicle localization using feature-based methods is a promising approach to achieve infrastructure-free high-accuracy localization. In this paper, we provide the first extensive evaluation of available feature extraction methods for this task, using separately taken image pairs as well as synthetic...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	165,892
2412.08398	Grasp Diffusion Network: Learning Grasp Generators from Partial Point Clouds with Diffusion Models in SO(3)xR3	Grasping objects successfully from a single-view camera is crucial in many robot manipulation tasks. An approach to solve this problem is to leverage simulation to create large datasets of pairs of objects and grasp poses, and then learn a conditional generative model that can be prompted quickly during deployment. How...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	516,071
2006.14320	Analyzing Effect of Repeated Reading on Oral Fluency and Narrative Production for Computer-Assisted Language Learning	Repeated reading (RR) helps learners, who have little to no experience with reading fluently to gain confidence, speed and process words automatically. The benefits of repeated readings include helping all learners with fact recall, aiding identification of learners' main ideas and vocabulary, increasing comprehension,...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	184,187
2501.10673	Hybrid-Quantum Neural Architecture Search for The Proximal Policy Optimization Algorithm	Recent studies in quantum machine learning advocated the use of hybrid models to assist with the limitations of the currently existing Noisy Intermediate Scale Quantum (NISQ) devices, but what was missing from most of them was the explanations and interpretations of the choices that were made to pick those exact archit...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	525,613
2007.05801	Migratable AI: Effect of identity and information migration on users perception of conversational AI agents	Conversational AI agents are proliferating, embodying a range of devices such as smart speakers, smart displays, robots, cars, and more. We can envision a future where a personal conversational agent could migrate across different form factors and environments to always accompany and assist its user to support a far mo...	true	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	186,792
2106.08727	AtrialGeneral: Domain Generalization for Left Atrial Segmentation of Multi-Center LGE MRIs	Left atrial (LA) segmentation from late gadolinium enhanced magnetic resonance imaging (LGE MRI) is a crucial step needed for planning the treatment of atrial fibrillation. However, automatic LA segmentation from LGE MRI is still challenging, due to the poor image quality, high variability in LA shapes, and unclear LA ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	241,400
2101.05716	SICKNL: A Dataset for Dutch Natural Language Inference	We present SICK-NL (read: signal), a dataset targeting Natural Language Inference in Dutch. SICK-NL is obtained by translating the SICK dataset of Marelli et al. (2014)from English into Dutch. Having a parallel inference dataset allows us to compare both monolingual and multilingual NLP models for English and Dutch on ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	215,508
1709.04747	T${}^2$K${}^2$: The Twitter Top-K Keywords Benchmark	Information retrieval from textual data focuses on the construction of vocabularies that contain weighted term tuples. Such vocabularies can then be exploited by various text analysis algorithms to extract new knowledge, e.g., top-k keywords, top-k documents, etc. Top-k keywords are casually used for various purposes, ...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	80,723
1506.00839	The Influence of Context on Dialogue Act Recognition	This article presents an analysis of the influence of context information on dialog act recognition. We performed experiments on the widely explored Switchboard corpus, as well as on data annotated according to the recent ISO 24617-2 standard. The latter was obtained from the Tilburg DialogBank and through the mapping ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	43,722
1706.06696	The NAO Backpack: An Open-hardware Add-on for Fast Software Development with the NAO Robot	We present an open-source accessory for the NAO robot, which enables to test computationally demanding algorithms in an external platform while preserving robot's autonomy and mobility. The platform has the form of a backpack, which can be 3D printed and replicated, and holds an ODROID XU4 board to process algorithms e...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	75,722
2211.06130	Physically Consistent Neural ODEs for Learning Multi-Physics Systems	Despite the immense success of neural networks in modeling system dynamics from data, they often remain physics-agnostic black boxes. In the particular case of physical systems, they might consequently make physically inconsistent predictions, which makes them unreliable in practice. In this paper, we leverage the fram...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	329,790
2106.05786	CAT: Cross Attention in Vision Transformer	Since Transformer has found widespread use in NLP, the potential of Transformer in CV has been realized and has inspired many new approaches. However, the computation required for replacing word tokens with image patches for Transformer after the tokenization of the image is vast(e.g., ViT), which bottlenecks model tra...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	240,226
2104.10218	Episodic Memory Model for Learning Robotic Manipulation Tasks	Machine learning, artificial intelligence and especially deep learning based approaches are often used to simplify or eliminate the burden of programming industrial robots. Using these approaches robots inherently learn a skill instead of being programmed using strict and tedious programming instructions. While deep le...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	231,499
2307.11336	Character Time-series Matching For Robust License Plate Recognition	Automatic License Plate Recognition (ALPR) is becoming a popular study area and is applied in many fields such as transportation or smart city. However, there are still several limitations when applying many current methods to practical problems due to the variation in real-world situations such as light changes, uncle...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	380,870
2407.08248	Toward accessible comics for blind and low vision readers	This work explores how to fine-tune large language models using prompt engineering techniques with contextual information for generating an accurate text description of the full story, ready to be forwarded to off-the-shelve speech synthesis tools. We propose to use existing computer vision and optical character recogn...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	472,086
2401.12941	Multicultural Name Recognition For Previously Unseen Names	State of the art Named Entity Recognition (NER) models have achieved an impressive ability to extract common phrases from text that belong to labels such as location, organization, time, and person. However, typical NER systems that rely on having seen a specific entity in their training data in order to label an entit...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	423,545
2409.05418	Distributed Optimization with Finite Bit Adaptive Quantization for Efficient Communication and Precision Enhancement	In realistic distributed optimization scenarios, individual nodes possess only partial information and communicate over bandwidth constrained channels. For this reason, the development of efficient distributed algorithms is essential. In our paper we addresses the challenge of unconstrained distributed optimization. In...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	486,768
2403.02651	Learning at the Speed of Wireless: Online Real-Time Learning for AI-Enabled MIMO in NextG	Integration of artificial intelligence (AI) and machine learning (ML) into the air interface has been envisioned as a key technology for next-generation (NextG) cellular networks. At the air interface, multiple-input multiple-output (MIMO) and its variants such as multi-user MIMO (MU-MIMO) and massive/full-dimension MI...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	434,888
2402.16607	GVA: Reconstructing Vivid 3D Gaussian Avatars from Monocular Videos	In this paper, we present a novel method that facilitates the creation of vivid 3D Gaussian avatars from monocular video inputs (GVA). Our innovation lies in addressing the intricate challenges of delivering high-fidelity human body reconstructions and aligning 3D Gaussians with human skin surfaces accurately. The key ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	432,621
2102.08327	Submodular Maximization subject to a Knapsack Constraint: Combinatorial Algorithms with Near-optimal Adaptive Complexity	Submodular maximization is a classic algorithmic problem with multiple applications in data mining and machine learning; there, the growing need to deal with massive instances motivates the design of algorithms balancing the quality of the solution with applicability. For the latter, an important measure is the adaptiv...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	220,412
1904.01783	Multi-task Learning for Chinese Word Usage Errors Detection	Chinese word usage errors often occur in non-native Chinese learners' writing. It is very helpful for non-native Chinese learners to detect them automatically when learning writing. In this paper, we propose a novel approach, which takes advantages of different auxiliary tasks, such as POS-tagging prediction and word l...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	126,249
1705.08584	MMD GAN: Towards Deeper Understanding of Moment Matching Network	Generative moment matching network (GMMN) is a deep generative model that differs from Generative Adversarial Network (GAN) by replacing the discriminator in GAN with a two-sample test based on kernel maximum mean discrepancy (MMD). Although some theoretical guarantees of MMD have been studied, the empirical performanc...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	74,048
1510.04209	Finite Uniform Bisimulations for Linear Systems with Finite Input Alphabets	We consider a class of systems over finite alphabets, namely discrete-time systems with linear dynamics and a finite input alphabet. We formulate a notion of finite uniform bisimulation, and motivate and propose a notion of regular finite uniform bisimulation. We derive sufficient conditions for the existence of finite...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	47,900
1604.00486	New extremal binary self-dual codes of lengths 64 and 66 from bicubic planar graphs	In this work, connected cubic planar bipartite graphs and related binary self-dual codes are studied. Binary self-dual codes of length 16 are obtained by face-vertex incidence matrices of these graphs. By considering their lifts to the ring R_2 new extremal binary self-dual codes of lengths 64 are constructed as Gray i...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	54,037
2103.12624	Genetic column generation: Fast computation of high-dimensional multi-marginal optimal transport problems	We introduce a simple, accurate, and extremely efficient method for numerically solving the multi-marginal optimal transport (MMOT) problems arising in density functional theory. The method relies on (i) the sparsity of optimal plans [for $N$ marginals discretized by $\ell$ gridpoints each, general Kantorovich plans re...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	226,241
2009.05147	Practical Cross-modal Manifold Alignment for Grounded Language	We propose a cross-modality manifold alignment procedure that leverages triplet loss to jointly learn consistent, multi-modal embeddings of language-based concepts of real-world items. Our approach learns these embeddings by sampling triples of anchor, positive, and negative data points from RGB-depth images and their ...	false	false	false	false	false	false	true	true	false	false	false	true	false	false	false	false	false	false	195,233
2405.09324	Learning Coarse-Grained Dynamics on Graph	We consider a Graph Neural Network (GNN) non-Markovian modeling framework to identify coarse-grained dynamical systems on graphs. Our main idea is to systematically determine the GNN architecture by inspecting how the leading term of the Mori-Zwanzig memory term depends on the coarse-grained interaction coefficients th...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	454,366
2502.14467	Provable Quantum Algorithm Advantage for Gaussian Process Quadrature	The aim of this paper is to develop novel quantum algorithms for Gaussian process quadrature methods. Gaussian process quadratures are numerical integration methods where Gaussian processes are used as functional priors for the integrands to capture the uncertainty arising from the sparse function evaluations. Quantum ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	535,844
1910.09679	Sparse Networks with Core-Periphery Structure	We propose a statistical model for graphs with a core-periphery structure. To do this we define a precise notion of what it means for a graph to have this structure, based on the sparsity properties of the subgraphs of core and periphery nodes. We present a class of sparse graphs with such properties, and provide metho...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	150,264
2304.05736	Communicating Uncertainty in Machine Learning Explanations: A Visualization Analytics Approach for Predictive Process Monitoring	As data-driven intelligent systems advance, the need for reliable and transparent decision-making mechanisms has become increasingly important. Therefore, it is essential to integrate uncertainty quantification and model explainability approaches to foster trustworthy business and operational process analytics. This st...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	357,730
1907.07315	A General Framework of Learning Multi-Vehicle Interaction Patterns from Videos	Semantic learning and understanding of multi-vehicle interaction patterns in a cluttered driving environment are essential but challenging for autonomous vehicles to make proper decisions. This paper presents a general framework to gain insights into intricate multi-vehicle interaction patterns from bird's-eye view tra...	false	false	false	false	false	false	true	true	false	false	false	true	false	false	false	false	false	false	138,841
2008.07725	SoDA: Multi-Object Tracking with Soft Data Association	Robust multi-object tracking (MOT) is a prerequisite fora safe deployment of self-driving cars. Tracking objects, however, remains a highly challenging problem, especially in cluttered autonomous driving scenes in which objects tend to interact with each other in complex ways and frequently get occluded. We propose a n...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	192,203
1912.12607	Towards Unified INT8 Training for Convolutional Neural Network	Recently low-bit (e.g., 8-bit) network quantization has been extensively studied to accelerate the inference. Besides inference, low-bit training with quantized gradients can further bring more considerable acceleration, since the backward process is often computation-intensive. Unfortunately, the inappropriate quantiz...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	158,883
2212.13295	Structure-based drug discovery with deep learning	Artificial intelligence (AI) in the form of deep learning bears promise for drug discovery and chemical biology, $\textit{e.g.}$, to predict protein structure and molecular bioactivity, plan organic synthesis, and design molecules $\textit{de novo}$. While most of the deep learning efforts in drug discovery have focuse...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	338,263
2411.07231	Watermark Anything with Localized Messages	Image watermarking methods are not tailored to handle small watermarked areas. This restricts applications in real-world scenarios where parts of the image may come from different sources or have been edited. We introduce a deep-learning model for localized image watermarking, dubbed the Watermark Anything Model (WAM)....	false	false	false	false	false	false	false	false	false	false	false	true	true	false	false	false	false	false	507,448
2311.06647	Robust Text Classification: Analyzing Prototype-Based Networks	Downstream applications often require text classification models to be accurate and robust. While the accuracy of the state-of-the-art Language Models (LMs) approximates human performance, they often exhibit a drop in performance on noisy data found in the real world. This lack of robustness can be concerning, as even ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	407,017
2102.04721	Classification of Imbalanced Credit scoring data sets Based on Ensemble Method with the Weighted-Hybrid-Sampling	In the era of big data, the utilization of credit-scoring models to determine the credit risk of applicants accurately becomes a trend in the future. The conventional machine learning on credit scoring data sets tends to have poor classification for the minority class, which may bring huge commercial harm to banks. In ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	219,205

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