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
2107.07919	A Survey on Bias in Visual Datasets	Computer Vision (CV) has achieved remarkable results, outperforming humans in several tasks. Nonetheless, it may result in significant discrimination if not handled properly as CV systems highly depend on the data they are fed with and can learn and amplify biases within such data. Thus, the problems of understanding a...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	246,565
1805.05101	Sparse Convolutional Beamforming for Ultrasound Imaging	The standard technique used by commercial medical ultrasound systems to form B-mode images is delay and sum (DAS) beamforming. However, DAS often results in limited image resolution and contrast, which are governed by the center frequency and the aperture size of the ultrasound transducer. A large number of elements le...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	97,381
cs/0002012	On The Closest String and Substring Problems	The problem of finding a center string that is `close' to every given string arises and has many applications in computational biology and coding theory. This problem has two versions: the Closest String problem and the Closest Substring problem. Assume that we are given a set of strings ${\cal S}=\{s_1, s_2, ..., s_n\...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	537,011
2406.13101	On instabilities in neural network-based physics simulators	When neural networks are trained from data to simulate the dynamics of physical systems, they encounter a persistent challenge: the long-time dynamics they produce are often unphysical or unstable. We analyze the origin of such instabilities when learning linear dynamical systems, focusing on the training dynamics. We ...	false	true	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	465,688
2202.12326	Towards Better Meta-Initialization with Task Augmentation for Kindergarten-aged Speech Recognition	Children's automatic speech recognition (ASR) is always difficult due to, in part, the data scarcity problem, especially for kindergarten-aged kids. When data are scarce, the model might overfit to the training data, and hence good starting points for training are essential. Recently, meta-learning was proposed to lear...	false	false	true	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	282,190
2403.06045	Sample-Optimal Zero-Violation Safety For Continuous Control	In this paper, we study the problem of ensuring safety with a few shots of samples for partially unknown systems. We first characterize a fundamental limit when producing safe actions is not possible due to insufficient information or samples. Then, we develop a technique that can generate provably safe actions and rec...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	436,272
2408.10653	UIE-UnFold: Deep Unfolding Network with Color Priors and Vision Transformer for Underwater Image Enhancement	Underwater image enhancement (UIE) plays a crucial role in various marine applications, but it remains challenging due to the complex underwater environment. Current learning-based approaches frequently lack explicit incorporation of prior knowledge about the physical processes involved in underwater image formation, r...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	481,961
2005.08519	Yseop at SemEval-2020 Task 5: Cascaded BERT Language Model for Counterfactual Statement Analysis	In this paper, we explore strategies to detect and evaluate counterfactual sentences. We describe our system for SemEval-2020 Task 5: Modeling Causal Reasoning in Language: Detecting Counterfactuals. We use a BERT base model for the classification task and build a hybrid BERT Multi-Layer Perceptron system to handle the...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	177,647
2104.14589	User-centric Cell-free Massive MIMO Networks: A Survey of Opportunities, Challenges and Solutions	Densification of network base stations is indispensable to achieve the stringent Quality of Service (QoS) requirements of future mobile networks. However, with a dense deployment of transmitters, interference management becomes an arduous task. To solve this issue, exploring radically new network architectures with int...	false	false	false	false	false	false	false	false	false	true	true	false	false	false	false	false	false	true	232,874
2204.05007	HiMODE: A Hybrid Monocular Omnidirectional Depth Estimation Model	Monocular omnidirectional depth estimation is receiving considerable research attention due to its broad applications for sensing 360{\deg} surroundings. Existing approaches in this field suffer from limitations in recovering small object details and data lost during the ground-truth depth map acquisition. In this pape...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	290,876
2403.08528	Pig aggression classification using CNN, Transformers and Recurrent Networks	The development of techniques that can be used to analyze and detect animal behavior is a crucial activity for the livestock sector, as it is possible to monitor the stress and animal welfare and contributes to decision making in the farm. Thus, the development of applications can assist breeders in making decisions to...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	437,373
2205.11739	Deep Learning Meets Software Engineering: A Survey on Pre-Trained Models of Source Code	Recent years have seen the successful application of deep learning to software engineering (SE). In particular, the development and use of pre-trained models of source code has enabled state-of-the-art results to be achieved on a wide variety of SE tasks. This paper provides an overview of this rapidly advancing field ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	298,259
2006.01889	Generating Random Logic Programs Using Constraint Programming	Testing algorithms across a wide range of problem instances is crucial to ensure the validity of any claim about one algorithm's superiority over another. However, when it comes to inference algorithms for probabilistic logic programs, experimental evaluations are limited to only a few programs. Existing methods to gen...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	179,885
1808.02531	SchiNet: Automatic Estimation of Symptoms of Schizophrenia from Facial Behaviour Analysis	Patients with schizophrenia often display impairments in the expression of emotion and speech and those are observed in their facial behaviour. Automatic analysis of patients' facial expressions that is aimed at estimating symptoms of schizophrenia has received attention recently. However, the datasets that are typical...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	104,785
2410.18413	AC-Network-Informed DC Optimal Power Flow for Electricity Markets	This paper presents a parametric quadratic approximation of the AC optimal power flow (AC-OPF) problem for time-sensitive and market-based applications. The parametric approximation preserves the physics-based but simple representation provided by the DC-OPF model and leverages market and physics information encoded in...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	501,877
1707.04724	Memoisation: Purely, Left-recursively, and with (Continuation Passing) Style	Memoisation, or tabling, is a well-known technique that yields large improvements in the performance of some recursive computations. Tabled resolution in Prologs such as XSB and B-Prolog can transform so called left-recursive predicates from non-terminating computations into finite and well-behaved ones. In the functio...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	77,095
2104.10093	Class-Incremental Learning with Generative Classifiers	Incrementally training deep neural networks to recognize new classes is a challenging problem. Most existing class-incremental learning methods store data or use generative replay, both of which have drawbacks, while 'rehearsal-free' alternatives such as parameter regularization or bias-correction methods do not consis...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	231,461
2001.11710	Context-Aware Deep Q-Network for Decentralized Cooperative Reconnaissance by a Robotic Swarm	One of the crucial problems in robotic swarm-based operation is to search and neutralize heterogeneous targets in an unknown and uncertain environment, without any communication within the swarm. Here, some targets can be neutralized by a single robot, while others need multiple robots in a particular sequence to neutr...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	162,145
2104.09088	Alexa Conversations: An Extensible Data-driven Approach for Building Task-oriented Dialogue Systems	Traditional goal-oriented dialogue systems rely on various components such as natural language understanding, dialogue state tracking, policy learning and response generation. Training each component requires annotations which are hard to obtain for every new domain, limiting scalability of such systems. Similarly, rul...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	231,112
2202.07231	Few-shot semantic segmentation via mask aggregation	Few-shot semantic segmentation aims to recognize novel classes with only very few labelled data. This challenging task requires mining of the relevant relationships between the query image and the support images. Previous works have typically regarded it as a pixel-wise classification problem. Therefore, various models...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	280,482
2409.15868	Privacy Evaluation Benchmarks for NLP Models	By inducing privacy attacks on NLP models, attackers can obtain sensitive information such as training data and model parameters, etc. Although researchers have studied, in-depth, several kinds of attacks in NLP models, they are non-systematic analyses. It lacks a comprehensive understanding of the impact caused by the...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	491,104
2309.04013	An Element-wise RSAV Algorithm for Unconstrained Optimization Problems	We present a novel optimization algorithm, element-wise relaxed scalar auxiliary variable (E-RSAV), that satisfies an unconditional energy dissipation law and exhibits improved alignment between the modified and the original energy. Our algorithm features rigorous proofs of linear convergence in the convex setting. Fur...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	390,583
1807.05688	SCAN: Self-and-Collaborative Attention Network for Video Person Re-identification	Video person re-identification attracts much attention in recent years. It aims to match image sequences of pedestrians from different camera views. Previous approaches usually improve this task from three aspects, including a) selecting more discriminative frames, b) generating more informative temporal representation...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	102,975
2310.12976	Exploring Invariance in Images through One-way Wave Equations	In this paper, we empirically reveal an invariance over images-images share a set of one-way wave equations with latent speeds. Each image is uniquely associated with a solution to these wave equations, allowing for its reconstruction with high fidelity from an initial condition. We demonstrate it using an intuitive en...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	401,229
2310.20366	Distil the informative essence of loop detector data set: Is network-level traffic forecasting hungry for more data?	Network-level traffic condition forecasting has been intensively studied for decades. Although prediction accuracy has been continuously improved with emerging deep learning models and ever-expanding traffic data, traffic forecasting still faces many challenges in practice. These challenges include the robustness of da...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	404,365
2001.01526	Mutual Mean-Teaching: Pseudo Label Refinery for Unsupervised Domain Adaptation on Person Re-identification	Person re-identification (re-ID) aims at identifying the same persons' images across different cameras. However, domain diversities between different datasets pose an evident challenge for adapting the re-ID model trained on one dataset to another one. State-of-the-art unsupervised domain adaptation methods for person ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	159,503
2110.10994	Interpretable Machine Learning for Resource Allocation with Application to Ventilator Triage	Rationing of healthcare resources is a challenging decision that policy makers and providers may be forced to make during a pandemic, natural disaster, or mass casualty event. Well-defined guidelines to triage scarce life-saving resources must be designed to promote transparency, trust, and consistency. To facilitate b...	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	262,327
2307.13424	Holistic Exploration on Universal Decompositional Semantic Parsing: Architecture, Data Augmentation, and LLM Paradigm	In this paper, we conduct a holistic exploration of the Universal Decompositional Semantic (UDS) Parsing. We first introduce a cascade model for UDS parsing that decomposes the complex parsing task into semantically appropriate subtasks. Our approach outperforms the prior models, while significantly reducing inference ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	381,583
2303.15046	Nighttime Smartphone Reflective Flare Removal Using Optical Center Symmetry Prior	Reflective flare is a phenomenon that occurs when light reflects inside lenses, causing bright spots or a "ghosting effect" in photos, which can impact their quality. Eliminating reflective flare is highly desirable but challenging. Many existing methods rely on manually designed features to detect these bright spots, ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	354,340
2412.04137	Text Change Detection in Multilingual Documents Using Image Comparison	Document comparison typically relies on optical character recognition (OCR) as its core technology. However, OCR requires the selection of appropriate language models for each document and the performance of multilingual or hybrid models remains limited. To overcome these challenges, we propose text change detection (T...	false	false	false	false	true	false	true	false	true	false	false	true	false	false	false	false	false	false	514,276
2007.08053	Inductive Link Prediction for Nodes Having Only Attribute Information	Predicting the link between two nodes is a fundamental problem for graph data analytics. In attributed graphs, both the structure and attribute information can be utilized for link prediction. Most existing studies focus on transductive link prediction where both nodes are already in the graph. However, many real-world...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	187,505
2207.04673	Learning Spatial and Temporal Variations for 4D Point Cloud Segmentation	LiDAR-based 3D scene perception is a fundamental and important task for autonomous driving. Most state-of-the-art methods on LiDAR-based 3D recognition tasks focus on single frame 3D point cloud data, and the temporal information is ignored in those methods. We argue that the temporal information across the frames prov...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	307,273
2411.13093	Video-RAG: Visually-aligned Retrieval-Augmented Long Video Comprehension	Existing large video-language models (LVLMs) struggle to comprehend long videos correctly due to limited context. To address this problem, fine-tuning long-context LVLMs and employing GPT-based agents have emerged as promising solutions. However, fine-tuning LVLMs would require extensive high-quality data and substanti...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	509,674
2410.20855	ByteNet: Rethinking Multimedia File Fragment Classification through Visual Perspectives	Multimedia file fragment classification (MFFC) aims to identify file fragment types, e.g., image/video, audio, and text without system metadata. It is of vital importance in multimedia storage and communication. Existing MFFC methods typically treat fragments as 1D byte sequences and emphasize the relations between sep...	false	false	false	false	false	false	false	false	false	false	false	true	true	false	false	false	false	true	502,995
2002.00614	Regularizers for Single-step Adversarial Training	The progress in the last decade has enabled machine learning models to achieve impressive performance across a wide range of tasks in Computer Vision. However, a plethora of works have demonstrated the susceptibility of these models to adversarial samples. Adversarial training procedure has been proposed to defend agai...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	162,409
1801.09749	Deep Learning based Retinal OCT Segmentation	Our objective is to evaluate the efficacy of methods that use deep learning (DL) for the automatic fine-grained segmentation of optical coherence tomography (OCT) images of the retina. OCT images from 10 patients with mild non-proliferative diabetic retinopathy were used from a public (U. of Miami) dataset. For each pa...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	89,163
2312.11862	Topo-MLP : A Simplicial Network Without Message Passing	Due to their ability to model meaningful higher order relations among a set of entities, higher order network models have emerged recently as a powerful alternative for graph-based network models which are only capable of modeling binary relationships. Message passing paradigm is still dominantly used to learn represen...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	416,750
1202.5657	Design of a Fractional Order Phase Shaper for Iso-damped Control of a PHWR under Step-back Condition	Phase shaping using fractional order (FO) phase shapers has been proposed by many contemporary researchers as a means of producing systems with iso-damped closed loop response due to a stepped variation in input. Such systems, with the closed loop damping remaining invariant to gain changes can be used to produce dead-...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	14,571
1905.10351	Tempus Volat, Hora Fugit -- A Survey of Tie-Oriented Dynamic Network Models in Discrete and Continuous Time	Given the growing number of available tools for modeling dynamic networks, the choice of a suitable model becomes central. The goal of this survey is to provide an overview of tie-oriented dynamic network models. The survey is focused on introducing binary network models with their corresponding assumptions, advantages...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	132,032
2205.04169	Multi-Fingered In-Hand Manipulation with Various Object Properties Using Graph Convolutional Networks and Distributed Tactile Sensors	Multi-fingered hands could be used to achieve many dexterous manipulation tasks, similarly to humans, and tactile sensing could enhance the manipulation stability for a variety of objects. However, tactile sensors on multi-fingered hands have a variety of sizes and shapes. Convolutional neural networks (CNN) can be use...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	295,560
2002.00666	Agent-Based Proof Design via Lemma Flow Diagram	We discuss an agent-based approach to proof design and implementation, which we call {\it Lemma Flow Diagram} (LFD). This approach is based on the multicut rule with $shared$ cuts. This approach is modular and easy to use, read and automate. Thus, we consider LFD an appealing alternative to `flow proof' which is popula...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	162,422
1703.01956	5G Wireless and Wired Convergence in a Passive Optical Network using UF-OFDM and GFDM	The provision of both wireless and wired services in the optical access domain will be an important function for future passive optical networks (PON). With the emergence of 5th generation (5G) mobile communications, a move toward a dense deployment of small cell antenna sites, in conjunction with a cloud radio access ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	69,460
2207.11177	Provable Defense Against Geometric Transformations	Geometric image transformations that arise in the real world, such as scaling and rotation, have been shown to easily deceive deep neural networks (DNNs). Hence, training DNNs to be certifiably robust to these perturbations is critical. However, no prior work has been able to incorporate the objective of deterministic ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	309,533
2105.07109	The Low-Dimensional Linear Geometry of Contextualized Word Representations	Black-box probing models can reliably extract linguistic features like tense, number, and syntactic role from pretrained word representations. However, the manner in which these features are encoded in representations remains poorly understood. We present a systematic study of the linear geometry of contextualized word...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	235,318
2210.11344	Towards Evology: a Market Ecology Agent-Based Model of US Equity Mutual Funds	The profitability of various investment styles in investment funds depends on macroeconomic conditions. Market ecology, which views financial markets as ecosystems of diverse, interacting and evolving trading strategies, has shown that endogenous interactions between strategies determine market behaviour and styles' pe...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	325,277
2410.08900	A Benchmark for Cross-Domain Argumentative Stance Classification on Social Media	Argumentative stance classification plays a key role in identifying authors' viewpoints on specific topics. However, generating diverse pairs of argumentative sentences across various domains is challenging. Existing benchmarks often come from a single domain or focus on a limited set of topics. Additionally, manual an...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	497,335
2309.15554	Direct Models for Simultaneous Translation and Automatic Subtitling: FBK@IWSLT2023	This paper describes the FBK's participation in the Simultaneous Translation and Automatic Subtitling tracks of the IWSLT 2023 Evaluation Campaign. Our submission focused on the use of direct architectures to perform both tasks: for the simultaneous one, we leveraged the knowledge already acquired by offline-trained mo...	false	false	true	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	395,015
2108.00612	Several classes of bent functions over finite fields	Let $\mathbb{F}_{p^{n}}$ be the finite field with $p^n$ elements and $\operatorname{Tr}(\cdot)$ be the trace function from $\mathbb{F}_{p^{n}}$ to $\mathbb{F}_{p}$, where $p$ is a prime and $n$ is an integer. Inspired by the works of Mesnager (IEEE Trans. Inf. Theory 60(7): 4397-4407, 2014) and Tang et al. (IEEE Trans....	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	248,774
2003.10675	Increasing Security Degree of Freedom in Multi-user and Multi-eve Systems	Secure communication in the Multi-user and Multi-eavesdropper (MUME) scenario is considered in this paper. It has be shown that secrecy can be improved when the transmitter simultaneously transmits information-bearing signal to the intended receivers and artificial noise to confuse the eavesdroppers. Several processing...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	169,410
2310.11589	Eliciting Human Preferences with Language Models	Language models (LMs) can be directed to perform target tasks by using labeled examples or natural language prompts. But selecting examples or writing prompts for can be challenging--especially in tasks that involve unusual edge cases, demand precise articulation of nebulous preferences, or require an accurate mental m...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	400,690
2002.07788	Multi-Issue Bargaining With Deep Reinforcement Learning	Negotiation is a process where agents aim to work through disputes and maximize their surplus. As the use of deep reinforcement learning in bargaining games is unexplored, this paper evaluates its ability to exploit, adapt, and cooperate to produce fair outcomes. Two actor-critic networks were trained for the bidding a...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	true	164,569
2102.08498	Pattern Sampling for Shapelet-based Time Series Classification	Subsequence-based time series classification algorithms provide accurate and interpretable models, but training these models is extremely computation intensive. The asymptotic time complexity of subsequence-based algorithms remains a higher-order polynomial, because these algorithms are based on exhaustive search for h...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	220,475
2105.11678	Hybrid Movie Recommender System based on Resource Allocation	Recommender Systems are inevitable to personalize user's experiences on the Internet. They are using different approaches to recommend the Top-K items to users according to their preferences. Nowadays recommender systems have become one of the most important parts of largescale data mining techniques. In this paper, we...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	236,783
2203.00111	Pedagogical Demonstrations and Pragmatic Learning in Artificial Tutor-Learner Interactions	When demonstrating a task, human tutors pedagogically modify their behavior by either "showing" the task rather than just "doing" it (exaggerating on relevant parts of the demonstration) or by giving demonstrations that best disambiguate the communicated goal. Analogously, human learners pragmatically infer the communi...	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	282,871
1801.06742	Multi-pseudo Regularized Label for Generated Data in Person Re-Identification	Sufficient training data normally is required to train deeply learned models. However, due to the expensive manual process for labelling large number of images, the amount of available training data is always limited. To produce more data for training a deep network, Generative Adversarial Network (GAN) can be used to ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	88,665
2202.05709	A Python Tool for Object-Centric Process Mining Comparison	Object-centric process mining provides a more holistic view of processes where we analyze processes with multiple case notions. However, most object-centric process mining techniques consider the whole event log rather than the comparison of existing behaviors in the log. In this paper, we introduce a stand-alone objec...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	279,959
2110.03659	Transform2Act: Learning a Transform-and-Control Policy for Efficient Agent Design	An agent's functionality is largely determined by its design, i.e., skeletal structure and joint attributes (e.g., length, size, strength). However, finding the optimal agent design for a given function is extremely challenging since the problem is inherently combinatorial and the design space is prohibitively large. A...	false	false	false	false	true	false	true	true	false	false	false	true	false	false	false	true	false	false	259,580
2406.17475	Performative Debias with Fair-exposure Optimization Driven by Strategic Agents in Recommender Systems	Data bias, e.g., popularity impairs the dynamics of two-sided markets within recommender systems. This overshadows the less visible but potentially intriguing long-tail items that could capture user interest. Despite the abundance of research surrounding this issue, it still poses challenges and remains a hot topic in ...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	467,589
2402.04655	Open-Vocabulary Calibration for Fine-tuned CLIP	Vision-language models (VLMs) have emerged as formidable tools, showing their strong capability in handling various open-vocabulary tasks in image recognition, text-driven visual content generation, and visual chatbots, to name a few. In recent years, considerable efforts and resources have been devoted to adaptation m...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	427,546
2404.16339	Training-Free Unsupervised Prompt for Vision-Language Models	Prompt learning has become the most effective paradigm for adapting large pre-trained vision-language models (VLMs) to downstream tasks. Recently, unsupervised prompt tuning methods, such as UPL and POUF, directly leverage pseudo-labels as supervisory information to fine-tune additional adaptation modules on unlabeled ...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	449,452
2412.17226	OLiDM: Object-aware LiDAR Diffusion Models for Autonomous Driving	To enhance autonomous driving safety in complex scenarios, various methods have been proposed to simulate LiDAR point cloud data. Nevertheless, these methods often face challenges in producing high-quality, diverse, and controllable foreground objects. To address the needs of object-aware tasks in 3D perception, we int...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	519,874
2307.01400	Spatio-Temporal Surrogates for Interaction of a Jet with High Explosives: Part II -- Clustering Extremely High-Dimensional Grid-Based Data	Building an accurate surrogate model for the spatio-temporal outputs of a computer simulation is a challenging task. A simple approach to improve the accuracy of the surrogate is to cluster the outputs based on similarity and build a separate surrogate model for each cluster. This clustering is relatively straightforwa...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	377,335
2106.15121	Face Sketch Synthesis via Semantic-Driven Generative Adversarial Network	Face sketch synthesis has made significant progress with the development of deep neural networks in these years. The delicate depiction of sketch portraits facilitates a wide range of applications like digital entertainment and law enforcement. However, accurate and realistic face sketch generation is still a challengi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	243,619
2406.05366	Regret Bounds for Episodic Risk-Sensitive Linear Quadratic Regulator	Risk-sensitive linear quadratic regulator is one of the most fundamental problems in risk-sensitive optimal control. In this paper, we study online adaptive control of risk-sensitive linear quadratic regulator in the finite horizon episodic setting. We propose a simple least-squares greedy algorithm and show that it ac...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	462,103
2110.12468	False Correlation Reduction for Offline Reinforcement Learning	Offline reinforcement learning (RL) harnesses the power of massive datasets for resolving sequential decision problems. Most existing papers only discuss defending against out-of-distribution (OOD) actions while we investigate a broader issue, the false correlations between epistemic uncertainty and decision-making, an...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	262,855
1102.2250	Modeling the pairwise key distribution scheme in the presence of unreliable links	We investigate the secure connectivity of wireless sensor networks under the pairwise key distribution scheme of Chan et al.. Unlike recent work which was carried out under the assumption of full visibility, here we assume a (simplified) communication model where unreliable wireless links are represented as on/off chan...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	9,113
2307.08319	Soft Curriculum for Learning Conditional GANs with Noisy-Labeled and Uncurated Unlabeled Data	Label-noise or curated unlabeled data is used to compensate for the assumption of clean labeled data in training the conditional generative adversarial network; however, satisfying such an extended assumption is occasionally laborious or impractical. As a step towards generative modeling accessible to everyone, we intr...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	379,763
1007.1270	How to Maximize User Satisfaction Degree in Multi-service IP Networks	Bandwidth allocation is a fundamental problem in communication networks. With current network moving towards the Future Internet model, the problem is further intensified as network traffic demanding far from exceeds network bandwidth capability. Maintaining a certain user satisfaction degree therefore becomes a challe...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	7,022
1910.07320	The Blessings of Multiple Causes: A Reply to Ogburn et al. (2019)	Ogburn et al. (2019, arXiv:1910.05438) discuss "The Blessings of Multiple Causes" (Wang and Blei, 2018, arXiv:1805.06826). Many of their remarks are interesting. But they also claim that the paper has "foundational errors" and that its "premise is...incorrect." These claims are not substantiated. There are no foundatio...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	149,575
1903.03692	Self-triggered Control for Safety Critical Systems using Control Barrier Functions	We propose a real-time control strategy that combines self-triggered control with Control Lyapunov Functions (CLF) and Control Barrier Functions (CBF). Similar to related works proposing CLF-CBF-based controllers, the computation of the controller is achieved by solving a Quadratic Program (QP). However, we propose a Z...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	123,788
1905.10328	Tiresias: Predicting Security Events Through Deep Learning	With the increased complexity of modern computer attacks, there is a need for defenders not only to detect malicious activity as it happens, but also to predict the specific steps that will be taken by an adversary when performing an attack. However this is still an open research problem, and previous research in predi...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	132,021
2210.08667	From Function to Failure	Failure Mode Reasoning (FMR) is a method for formal analysis of system-related faults. The method was originally developed for identifying failure modes of safety-critical systems based on an analysis of their programs. In this paper, we generalize the method and present a mathematical framework for its use in model-ba...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	324,241
1903.10453	dpUGC: Learn Differentially Private Representation for User Generated Contents	This paper firstly proposes a simple yet efficient generalized approach to apply differential privacy to text representation (i.e., word embedding). Based on it, we propose a user-level approach to learn personalized differentially private word embedding model on user generated contents (UGC). To our best knowledge, th...	false	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	125,279
1507.02921	Zero Attracting PNLMS Algorithm and Its Convergence in Mean	The proportionate normalized least mean square (PNLMS) algorithm and its variants are by far the most popular adaptive filters that are used to identify sparse systems. The convergence speed of the PNLMS algorithm, though very high initially, however, slows down at a later stage, even becoming worse than sparsity agnos...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	45,032
2103.02140	PML: Progressive Margin Loss for Long-tailed Age Classification	In this paper, we propose a progressive margin loss (PML) approach for unconstrained facial age classification. Conventional methods make strong assumption on that each class owns adequate instances to outline its data distribution, likely leading to bias prediction where the training samples are sparse across age clas...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	222,856
2106.07470	Comparing vector fields across surfaces: interest for characterizing the orientations of cortical folds	Vectors fields defined on surfaces constitute relevant and useful representations but are rarely used. One reason might be that comparing vector fields across two surfaces of the same genus is not trivial: it requires to transport the vector fields from the original surfaces onto a common domain. In this paper, we prop...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	240,933
2103.10600	GCN-ALP: Addressing Matching Collisions in Anchor Link Prediction	Nowadays online users prefer to join multiple social media for the purpose of socialized online service. The problem \textit{anchor link prediction} is formalized to link user data with the common ground on user profile, content and network structure across social networks. Most of the traditional works concentrated on...	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	false	225,506
2102.07156	ChipNet: Budget-Aware Pruning with Heaviside Continuous Approximations	Structured pruning methods are among the effective strategies for extracting small resource-efficient convolutional neural networks from their dense counterparts with minimal loss in accuracy. However, most existing methods still suffer from one or more limitations, that include 1) the need for training the dense model...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	220,012
2203.16763	CREATE: A Benchmark for Chinese Short Video Retrieval and Title Generation	Previous works of video captioning aim to objectively describe the video's actual content, which lacks subjective and attractive expression, limiting its practical application scenarios. Video titling is intended to achieve this goal, but there is a lack of a proper benchmark. In this paper, we propose to CREATE, the f...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	288,906
2111.02529	Shift Happens: Adjusting Classifiers	Minimizing expected loss measured by a proper scoring rule, such as Brier score or log-loss (cross-entropy), is a common objective while training a probabilistic classifier. If the data have experienced dataset shift where the class distributions change post-training, then often the model's performance will decrease, o...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	264,898
2108.06685	Vector-Decomposed Disentanglement for Domain-Invariant Object Detection	To improve the generalization of detectors, for domain adaptive object detection (DAOD), recent advances mainly explore aligning feature-level distributions between the source and single-target domain, which may neglect the impact of domain-specific information existing in the aligned features. Towards DAOD, it is impo...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	250,682
1909.11512	Synthetic Data for Deep Learning	Synthetic data is an increasingly popular tool for training deep learning models, especially in computer vision but also in other areas. In this work, we attempt to provide a comprehensive survey of the various directions in the development and application of synthetic data. First, we discuss synthetic datasets for bas...	false	false	false	false	false	false	true	false	false	false	false	true	true	false	false	false	false	false	146,832
2408.02210	ExoViP: Step-by-step Verification and Exploration with Exoskeleton Modules for Compositional Visual Reasoning	Compositional visual reasoning methods, which translate a complex query into a structured composition of feasible visual tasks, have exhibited a strong potential in complicated multi-modal tasks. Empowered by recent advances in large language models (LLMs), this multi-modal challenge has been brought to a new stage by ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	478,543
2410.20210	Looking Beyond The Top-1: Transformers Determine Top Tokens In Order	Understanding the inner workings of Transformers is crucial for achieving more accurate and efficient predictions. In this work, we analyze the computation performed by Transformers in the layers after the top-1 prediction has become fixed, which has been previously referred to as the "saturation event". We expand the ...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	502,701
2402.09392	LL-GABR: Energy Efficient Live Video Streaming Using Reinforcement Learning	Over the recent years, research and development in adaptive bitrate (ABR) algorithms for live video streaming have been successful in improving users' quality of experience (QoE) by reducing latency to near real-time levels while delivering higher bitrate videos with minimal rebuffering time. However, the QoE models us...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	429,500
1208.2015	Sharp analysis of low-rank kernel matrix approximations	We consider supervised learning problems within the positive-definite kernel framework, such as kernel ridge regression, kernel logistic regression or the support vector machine. With kernels leading to infinite-dimensional feature spaces, a common practical limiting difficulty is the necessity of computing the kernel ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	18,011
2001.00784	Optimizing Wireless Systems Using Unsupervised and Reinforced-Unsupervised Deep Learning	Resource allocation and transceivers in wireless networks are usually designed by solving optimization problems subject to specific constraints, which can be formulated as variable or functional optimization. If the objective and constraint functions of a variable optimization problem can be derived, standard numerical...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	159,323
2305.16475	Initialization-Dependent Sample Complexity of Linear Predictors and Neural Networks	We provide several new results on the sample complexity of vector-valued linear predictors (parameterized by a matrix), and more generally neural networks. Focusing on size-independent bounds, where only the Frobenius norm distance of the parameters from some fixed reference matrix $W_0$ is controlled, we show that the...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	368,103
1907.11039	Visualization of Emergency Department Clinical Data for Interpretable Patient Phenotyping	Visual summarization of clinical data collected on patients contained within the electronic health record (EHR) may enable precise and rapid triage at the time of patient presentation to an emergency department (ED). The triage process is critical in the appropriate allocation of resources and in anticipating eventual ...	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	139,767
2202.01454	Deep Hierarchy in Bandits	Mean rewards of actions are often correlated. The form of these correlations may be complex and unknown a priori, such as the preferences of a user for recommended products and their categories. To maximize statistical efficiency, it is important to leverage these correlations when learning. We formulate a bandit varia...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	278,487
2102.12066	On the Minimal Error of Empirical Risk Minimization	We study the minimal error of the Empirical Risk Minimization (ERM) procedure in the task of regression, both in the random and the fixed design settings. Our sharp lower bounds shed light on the possibility (or impossibility) of adapting to simplicity of the model generating the data. In the fixed design setting, we s...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	221,599
2108.02842	Multimodal Meta-Learning for Time Series Regression	Recent work has shown the efficiency of deep learning models such as Fully Convolutional Networks (FCN) or Recurrent Neural Networks (RNN) to deal with Time Series Regression (TSR) problems. These models sometimes need a lot of data to be able to generalize, yet the time series are sometimes not long enough to be able ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	249,467
2003.06849	Deep Affinity Net: Instance Segmentation via Affinity	Most of the modern instance segmentation approaches fall into two categories: region-based approaches in which object bounding boxes are detected first and later used in cropping and segmenting instances; and keypoint-based approaches in which individual instances are represented by a set of keypoints followed by a den...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	168,249
1911.08715	An Inception Inspired Deep Network to Analyse Fundus Images	A fundus image usually contains the optic disc, pathologies and other structures in addition to vessels to be segmented. This study proposes a deep network for vessel segmentation, whose architecture is inspired by inception modules. The network contains three sub-networks, each with a different filter size, which are ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	154,279
2407.18808	Learning Chaotic Systems and Long-Term Predictions with Neural Jump ODEs	The Path-dependent Neural Jump ODE (PD-NJ-ODE) is a model for online prediction of generic (possibly non-Markovian) stochastic processes with irregular (in time) and potentially incomplete (with respect to coordinates) observations. It is a model for which convergence to the $L^2$-optimal predictor, which is given by t...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	476,522
1601.02658	Information-theoretic thresholds for community detection in sparse networks	We give upper and lower bounds on the information-theoretic threshold for community detection in the stochastic block model. Specifically, let $k$ be the number of groups, $d$ be the average degree, the probability of edges between vertices within and between groups be $c_\mathrm{in}/n$ and $c_\mathrm{out}/n$ respectiv...	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	true	50,849
2403.04810	Restricted Bayesian Neural Network	Modern deep learning tools are remarkably effective in addressing intricate problems. However, their operation as black-box models introduces increased uncertainty in predictions. Additionally, they contend with various challenges, including the need for substantial storage space in large networks, issues of overfittin...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	true	false	false	435,752
1906.03387	A Novel Modeling Approach for All-Dielectric Metasurfaces Using Deep Neural Networks	Metasurfaces have become a promising means for manipulating optical wavefronts in flat and high-performance optical devices. Conventional metasurface device design relies on trial-and-error methods to obtain target electromagnetic (EM) response, an approach that demands significant efforts to investigate the enormous n...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	134,364
2209.02384	Data Centred Intelligent Geosciences: Research Agenda and Opportunities, Position Paper	This paper describes and discusses our vision to develop and reason about best practices and novel ways of curating data-centric geosciences knowledge (data, experiments, models, methods, conclusions, and interpretations). This knowledge is produced from applying statistical modelling, Machine Learning, and modern data...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	316,185
2202.00866	Decoupled IoU Regression for Object Detection	Non-maximum suppression (NMS) is widely used in object detection pipelines for removing duplicated bounding boxes. The inconsistency between the confidence for NMS and the real localization confidence seriously affects detection performance. Prior works propose to predict Intersection-over-Union (IoU) between bounding ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	278,286

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