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
1812.06423	Classifier and Exemplar Synthesis for Zero-Shot Learning	Zero-shot learning (ZSL) enables solving a task without the need to see its examples. In this paper, we propose two ZSL frameworks that learn to synthesize parameters for novel unseen classes. First, we propose to cast the problem of ZSL as learning manifold embeddings from graphs composed of object classes, leading to...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	116,616
1105.1033	Adaptively Learning the Crowd Kernel	We introduce an algorithm that, given n objects, learns a similarity matrix over all n^2 pairs, from crowdsourced data alone. The algorithm samples responses to adaptively chosen triplet-based relative-similarity queries. Each query has the form "is object 'a' more similar to 'b' or to 'c'?" and is chosen to be maximal...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	10,259
2001.06882	Peer-to-Peer Trading in Electricity Networks: An Overview	Peer-to-peer trading is a next-generation energy management technique that economically benefits proactive consumers (prosumers) transacting their energy as goods and services. At the same time, peer-to-peer energy trading is also expected to help the grid by reducing peak demand, lowering reserve requirements, and cur...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	160,911
2008.13642	Radar+RGB Attentive Fusion for Robust Object Detection in Autonomous Vehicles	This paper presents two variations of architecture referred to as RANet and BIRANet. The proposed architecture aims to use radar signal data along with RGB camera images to form a robust detection network that works efficiently, even in variable lighting and weather conditions such as rain, dust, fog, and others. First...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	193,903
2404.04522	Q-PEFT: Query-dependent Parameter Efficient Fine-tuning for Text Reranking with Large Language Models	Parameter Efficient Fine-Tuning (PEFT) methods have been extensively utilized in Large Language Models (LLMs) to improve the down-streaming tasks without the cost of fine-tuing the whole LLMs. Recent studies have shown how to effectively use PEFT for fine-tuning LLMs in ranking tasks with convincing performance; there ...	false	false	false	false	true	true	true	false	true	false	false	false	false	false	false	false	false	false	444,681
2207.03422	AsNER -- Annotated Dataset and Baseline for Assamese Named Entity recognition	We present the AsNER, a named entity annotation dataset for low resource Assamese language with a baseline Assamese NER model. The dataset contains about 99k tokens comprised of text from the speech of the Prime Minister of India and Assamese play. It also contains person names, location names and addresses. The propos...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	306,837
2405.10658	Cost-Effective Fault Tolerance for CNNs Using Parameter Vulnerability Based Hardening and Pruning	Convolutional Neural Networks (CNNs) have become integral in safety-critical applications, thus raising concerns about their fault tolerance. Conventional hardware-dependent fault tolerance methods, such as Triple Modular Redundancy (TMR), are computationally expensive, imposing a remarkable overhead on CNNs. Whereas f...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	454,847
1911.04060	Invariant Representations through Adversarial Forgetting	We propose a novel approach to achieving invariance for deep neural networks in the form of inducing amnesia to unwanted factors of data through a new adversarial forgetting mechanism. We show that the forgetting mechanism serves as an information-bottleneck, which is manipulated by the adversarial training to learn in...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	152,873
2309.09975	GEDepth: Ground Embedding for Monocular Depth Estimation	Monocular depth estimation is an ill-posed problem as the same 2D image can be projected from infinite 3D scenes. Although the leading algorithms in this field have reported significant improvement, they are essentially geared to the particular compound of pictorial observations and camera parameters (i.e., intrinsics ...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	392,813
2002.03862	Cross-modal variational inference for bijective signal-symbol translation	Extraction of symbolic information from signals is an active field of research enabling numerous applications especially in the Musical Information Retrieval domain. This complex task, that is also related to other topics such as pitch extraction or instrument recognition, is a demanding subject that gave birth to nume...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	163,434
1911.02621	The Threat of Adversarial Attacks on Machine Learning in Network Security -- A Survey	Machine learning models have made many decision support systems to be faster, more accurate, and more efficient. However, applications of machine learning in network security face a more disproportionate threat of active adversarial attacks compared to other domains. This is because machine learning applications in net...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	true	152,402
2203.02458	Continuous Rating as Reliable Human Evaluation of Simultaneous Speech Translation	Simultaneous speech translation (SST) can be evaluated on simulated online events where human evaluators watch subtitled videos and continuously express their satisfaction by pressing buttons (so called Continuous Rating). Continuous Rating is easy to collect, but little is known about its reliability, or relation to c...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	283,749
2304.10808	Downstream Task-Oriented Neural Tokenizer Optimization with Vocabulary Restriction as Post Processing	This paper proposes a method to optimize tokenization for the performance improvement of already trained downstream models. Our method generates tokenization results attaining lower loss values of a given downstream model on the training data for restricting vocabularies and trains a tokenizer reproducing the tokenizat...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	359,565
1103.3742	The key exchange cryptosystem used with higher order Diophantine equations	One-way functions are widely used for encrypting the secret in public key cryptography, although they are regarded as plausibly one-way but have not been proven so. Here we discuss the public key cryptosystem based on the system of higher order Diophantine equations. In this system those Diophantine equations are used ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	9,671
2006.03617	A combined XFEM phase-field computational model for crack growth without remeshing	This paper presents an adaptive strategy for phase-field simulations with transition to fracture. The phase-field equations are solved only in small subdomains around crack tips to determine propagation, while an XFEM discretization is used in the rest of the domain to represent sharp cracks, enabling to use a coarser ...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	180,364
2307.10247	Automated Action Model Acquisition from Narrative Texts	Action models, which take the form of precondition/effect axioms, facilitate causal and motivational connections between actions for AI agents. Action model acquisition has been identified as a bottleneck in the application of planning technology, especially within narrative planning. Acquiring action models from narra...	false	false	false	false	false	true	true	false	true	false	false	false	false	false	false	false	false	false	380,487
2302.10747	Clustered Data Sharing for Non-IID Federated Learning over Wireless Networks	Federated Learning (FL) is a novel distributed machine learning approach to leverage data from Internet of Things (IoT) devices while maintaining data privacy. However, the current FL algorithms face the challenges of non-independent and identically distributed (non-IID) data, which causes high communication costs and ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	346,924
2406.18512	"Is ChatGPT a Better Explainer than My Professor?": Evaluating the Explanation Capabilities of LLMs in Conversation Compared to a Human Baseline	Explanations form the foundation of knowledge sharing and build upon communication principles, social dynamics, and learning theories. We focus specifically on conversational approaches for explanations because the context is highly adaptive and interactive. Our research leverages previous work on explanatory acts, a f...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	468,031
1206.0418	De-randomizing Shannon: The Design and Analysis of a Capacity-Achieving Rateless Code	This paper presents an analysis of spinal codes, a class of rateless codes proposed recently. We prove that spinal codes achieve Shannon capacity for the binary symmetric channel (BSC) and the additive white Gaussian noise (AWGN) channel with an efficient polynomial-time encoder and decoder. They are the first rateless...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	16,298
2207.13191	GCN-WP -- Semi-Supervised Graph Convolutional Networks for Win Prediction in Esports	Win prediction is crucial to understanding skill modeling, teamwork and matchmaking in esports. In this paper we propose GCN-WP, a semi-supervised win prediction model for esports based on graph convolutional networks. This model learns the structure of an esports league over the course of a season (1 year) and makes p...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	310,222
2404.00327	YNetr: Dual-Encoder architecture on Plain Scan Liver Tumors (PSLT)	Background: Liver tumors are abnormal growths in the liver that can be either benign or malignant, with liver cancer being a significant health concern worldwide. However, there is no dataset for plain scan segmentation of liver tumors, nor any related algorithms. To fill this gap, we propose Plain Scan Liver Tumors(PS...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	442,868
2301.11546	Adapting Step-size: A Unified Perspective to Analyze and Improve Gradient-based Methods for Adversarial Attacks	Learning adversarial examples can be formulated as an optimization problem of maximizing the loss function with some box-constraints. However, for solving this induced optimization problem, the state-of-the-art gradient-based methods such as FGSM, I-FGSM and MI-FGSM look different from their original methods especially...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	342,189
2209.14829	Lightweight Monocular Depth Estimation with an Edge Guided Network	Monocular depth estimation is an important task that can be applied to many robotic applications. Existing methods focus on improving depth estimation accuracy via training increasingly deeper and wider networks, however these suffer from large computational complexity. Recent studies found that edge information are im...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	320,366
1808.06470	predictSLUMS: A new model for identifying and predicting informal settlements and slums in cities from street intersections using machine learning	Identifying current and future informal regions within cities remains a crucial issue for policymakers and governments in developing countries. The delineation process of identifying such regions in cities requires a lot of resources. While there are various studies that identify informal settlements based on satellite...	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	105,538
2312.06395	Threshold Decision-Making Dynamics Adaptive to Physical Constraints and Changing Environment	We propose a threshold decision-making framework for controlling the physical dynamics of an agent switching between two spatial tasks. Our framework couples a nonlinear opinion dynamics model that represents the evolution of an agent's preference for a particular task with the physical dynamics of the agent. We prove ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	414,500
2009.07024	Decision-based Universal Adversarial Attack	A single perturbation can pose the most natural images to be misclassified by classifiers. In black-box setting, current universal adversarial attack methods utilize substitute models to generate the perturbation, then apply the perturbation to the attacked model. However, this transfer often produces inferior results....	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	195,813
2303.12980	GMP-Featurizer: A parallelized Python package for efficiently computing the Gaussian Multipole features of atomic systems	GMP-Featurizer is a lightweight, accurate, efficient, and scalable software package for calculating the Gaussian Multipole (GMP) features \cite{GMP} for a variety of atomic systems with elements across the periodic table. Starting from the GMP feature computation module from AmpTorch \cite{amptorch}, the capability of ...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	353,484
2305.05378	PLM-GNN: A Webpage Classification Method based on Joint Pre-trained Language Model and Graph Neural Network	The number of web pages is growing at an exponential rate, accumulating massive amounts of data on the web. It is one of the key processes to classify webpages in web information mining. Some classical methods are based on manually building features of web pages and training classifiers based on machine learning or dee...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	363,126
1908.00751	Merging variables: one technique of search in pseudo-Boolean optimization	In the present paper we describe new heuristic technique, which can be applied to the optimization of pseudo-Boolean functions including Black-Box functions. This technique is based on a simple procedure which consists in transition from the optimization problem over Boolean hypercube to the optimization problem of aux...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	140,590
2501.07764	Deep Learning for Disease Outbreak Prediction: A Robust Early Warning Signal for Transcritical Bifurcations	Early Warning Signals (EWSs) are vital for implementing preventive measures before a disease turns into a pandemic. While new diseases exhibit unique behaviors, they often share fundamental characteristics from a dynamical systems perspective. Moreover, measurements during disease outbreaks are often corrupted by diffe...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	524,502
1902.06667	Hierarchical Graph Convolutional Networks for Semi-supervised Node Classification	Graph convolutional networks (GCNs) have been successfully applied in node classification tasks of network mining. However, most of these models based on neighborhood aggregation are usually shallow and lack the "graph pooling" mechanism, which prevents the model from obtaining adequate global information. In order to ...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	121,808
1110.0061	Learning image transformations without training examples	The use of image transformations is essential for efficient modeling and learning of visual data. But the class of relevant transformations is large: affine transformations, projective transformations, elastic deformations, ... the list goes on. Therefore, learning these transformations, rather than hand coding them, i...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	12,433
2207.07484	Multi-AGV's Temporal Memory-based RRT Exploration in Unknown Environment	With the increasing need for multi-robot for exploring the unknown region in a challenging environment, efficient collaborative exploration strategies are needed for achieving such feat. A frontier-based Rapidly-Exploring Random Tree (RRT) exploration can be deployed to explore an unknown environment. However, its' gre...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	308,214
1709.00621	Cognitive Connectivity Resilience in Multi-layer Remotely Deployed Mobile Internet of Things	Enabling the Internet of things in remote areas without traditional communication infrastructure requires a multi-layer network architecture. The devices in the overlay network are required to provide coverage to the underlay devices as well as to remain connected to other overlay devices. The coordination, planning, a...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	79,936
2312.07831	Abusive Span Detection for Vietnamese Narrative Texts	Abuse in its various forms, including physical, psychological, verbal, sexual, financial, and cultural, has a negative impact on mental health. However, there are limited studies on applying natural language processing (NLP) in this field in Vietnam. Therefore, we aim to contribute by building a human-annotated Vietnam...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	415,071
2411.07490	$\textit{Dirigo}$: A Method to Extract Event Logs for Object-Centric Processes	Real-world processes involve multiple object types with intricate interrelationships. Traditional event logs (in XES format), which record process execution centred around the case notion, are restricted to a single-object perspective, making it difficult to capture the behaviour of multiple objects and their interacti...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	507,549
2310.04681	VoiceExtender: Short-utterance Text-independent Speaker Verification with Guided Diffusion Model	Speaker verification (SV) performance deteriorates as utterances become shorter. To this end, we propose a new architecture called VoiceExtender which provides a promising solution for improving SV performance when handling short-duration speech signals. We use two guided diffusion models, the built-in and the external...	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	397,763
1706.07094	Constrained Bayesian Optimization with Noisy Experiments	Randomized experiments are the gold standard for evaluating the effects of changes to real-world systems. Data in these tests may be difficult to collect and outcomes may have high variance, resulting in potentially large measurement error. Bayesian optimization is a promising technique for efficiently optimizing multi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	75,792
2211.05773	Scaling Neural Face Synthesis to High FPS and Low Latency by Neural Caching	Recent neural rendering approaches greatly improve image quality, reaching near photorealism. However, the underlying neural networks have high runtime, precluding telepresence and virtual reality applications that require high resolution at low latency. The sequential dependency of layers in deep networks makes their ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	329,672
2105.03037	ConCAD: Contrastive Learning-based Cross Attention for Sleep Apnea Detection	With recent advancements in deep learning methods, automatically learning deep features from the original data is becoming an effective and widespread approach. However, the hand-crafted expert knowledge-based features are still insightful. These expert-curated features can increase the model's generalization and remin...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	234,010
2403.01270	A comprehensive cross-language framework for harmful content detection with the aid of sentiment analysis	In today's digital world, social media plays a significant role in facilitating communication and content sharing. However, the exponential rise in user-generated content has led to challenges in maintaining a respectful online environment. In some cases, users have taken advantage of anonymity in order to use harmful ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	434,339
2004.09056	Colonoscope tracking method based on shape estimation network	This paper presents a colonoscope tracking method utilizing a colon shape estimation method. CT colonography is used as a less-invasive colon diagnosis method. If colonic polyps or early-stage cancers are found, they are removed in a colonoscopic examination. In the colonoscopic examination, understanding where the col...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	173,244
2405.13166	FairLENS: Assessing Fairness in Law Enforcement Speech Recognition	Automatic speech recognition (ASR) techniques have become powerful tools, enhancing efficiency in law enforcement scenarios. To ensure fairness for demographic groups in different acoustic environments, ASR engines must be tested across a variety of speakers in realistic settings. However, describing the fairness discr...	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	455,829
2402.01076	DoseGNN: Improving the Performance of Deep Learning Models in Adaptive Dose-Volume Histogram Prediction through Graph Neural Networks	Dose-Volume Histogram (DVH) prediction is fundamental in radiation therapy that facilitate treatment planning, dose evaluation, plan comparison and etc. It helps to increase the ability to deliver precise and effective radiation treatments while managing potential toxicities to healthy tissues as needed to reduce the r...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	425,844
2312.04966	NewMove: Customizing text-to-video models with novel motions	We introduce an approach for augmenting text-to-video generation models with customized motions, extending their capabilities beyond the motions depicted in the original training data. By leveraging a few video samples demonstrating specific movements as input, our method learns and generalizes the input motion pattern...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	413,906
2412.09686	The Cost of Replicability in Active Learning	Active learning aims to reduce the required number of labeled data for machine learning algorithms by selectively querying the labels of initially unlabeled data points. Ensuring the replicability of results, where an algorithm consistently produces the same outcome across different runs, is essential for the reliabili...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	516,597
2105.09232	Diffusion Approximations for Thompson Sampling	We study the behavior of Thompson sampling from the perspective of weak convergence. In the regime where the gaps between arm means scale as $1/\sqrt{n}$ with the time horizon $n$, we show that the dynamics of Thompson sampling evolve according to discrete versions of SDE's and stochastic ODE's. As $n \to \infty$, we s...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	236,006
2108.05792	From market-ready ROVs to low-cost AUVs	Autonomous Underwater Vehicles (AUVs) are becoming increasingly important for different types of industrial applications. The generally high cost of (AUVs) restricts the access to them and therefore advances in research and technological development. However, recent advances have led to lower cost commercially availabl...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	250,410
1807.02842	Auto-Context R-CNN	Region-based convolutional neural networks (R-CNN)~\cite{fast_rcnn,faster_rcnn,mask_rcnn} have largely dominated object detection. Operators defined on RoIs (Region of Interests) play an important role in R-CNNs such as RoIPooling~\cite{fast_rcnn} and RoIAlign~\cite{mask_rcnn}. They all only utilize information inside ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	102,355
1603.01113	A Survey of the State of the Art in Data Mining and Integration Query Languages	The major aim of this survey is to identify the strengths and weaknesses of a representative set of Data-Mining and Integration (DMI) query languages. We describe a set of properties of DMI-related languages that we use for a systematic evaluation of these languages. In addition, we introduce a scoring system that we u...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	52,858
2302.13186	Construction numbers: How to build a graph?	A construction sequence for a graph is a listing of the elements of the graph (the set of vertices and edges) such that each edge follows both its endpoints. The construction number of the graph is the number of such sequences. We determine this number for various graph families.	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	347,850
2311.04322	NEAT-MUSIC: Auto-calibration of DOA Estimation for Terahertz-Band Massive MIMO Systems	Terahertz (THz) band is envisioned for the future sixth generation wireless systems thanks to its abundant bandwidth and very narrow beamwidth. These features are one of the key enabling factors for high resolution sensing with milli-degree level direction-of-arrival (DOA) estimation. Therefore, this paper investigates...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	406,182
2501.14615	Single-neuron deep generative model uncovers underlying physics of neuronal activity in Ca imaging data	Calcium imaging has become a powerful alternative to electrophysiology for studying neuronal activity, offering spatial resolution and the ability to measure large populations of neurons in a minimally invasive manner. This technique has broad applications in neuroscience, neuroengineering, and medicine, enabling resea...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	527,186
2310.16662	Deep Learning Techniques for Cervical Cancer Diagnosis based on Pathology and Colposcopy Images	Cervical cancer is a prevalent disease affecting millions of women worldwide every year. It requires significant attention, as early detection during the precancerous stage provides an opportunity for a cure. The screening and diagnosis of cervical cancer rely on cytology and colposcopy methods. Deep learning, a promis...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	402,822
2410.20731	BLAPose: Enhancing 3D Human Pose Estimation with Bone Length Adjustment	Current approaches in 3D human pose estimation primarily focus on regressing 3D joint locations, often neglecting critical physical constraints such as bone length consistency and body symmetry. This work introduces a recurrent neural network architecture designed to capture holistic information across entire video seq...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	502,937
2211.00985	Distributed Robotic Systems in the Edge-Cloud Continuum with ROS 2: a Review on Novel Architectures and Technology Readiness	Robotic systems are more connected, networked, and distributed than ever. New architectures that comply with the \textit{de facto} robotics middleware standard, ROS\,2, have recently emerged to fill the gap in terms of hybrid systems deployed from edge to cloud. This paper reviews new architectures and technologies tha...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	328,073
2103.01428	Botcha: Detecting Malicious Non-Human Traffic in the Wild	Malicious bots make up about a quarter of all traffic on the web, and degrade the performance of personalization and recommendation algorithms that operate on e-commerce sites. Positive-Unlabeled learning (PU learning) provides the ability to train a binary classifier using only positive (P) and unlabeled (U) instances...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	222,611
1206.4601	Convex Multitask Learning with Flexible Task Clusters	Traditionally, multitask learning (MTL) assumes that all the tasks are related. This can lead to negative transfer when tasks are indeed incoherent. Recently, a number of approaches have been proposed that alleviate this problem by discovering the underlying task clusters or relationships. However, they are limited to ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	16,653
1507.03928	Pseudo-Query Reformulation	Automatic query reformulation refers to rewriting a user's original query in order to improve the ranking of retrieval results compared to the original query. We present a general framework for automatic query reformulation based on discrete optimization. Our approach, referred to as pseudo-query reformulation, treats ...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	45,118
2209.04521	The Space of Adversarial Strategies	Adversarial examples, inputs designed to induce worst-case behavior in machine learning models, have been extensively studied over the past decade. Yet, our understanding of this phenomenon stems from a rather fragmented pool of knowledge; at present, there are a handful of attacks, each with disparate assumptions in t...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	316,801
1711.09224	CondenseNet: An Efficient DenseNet using Learned Group Convolutions	Deep neural networks are increasingly used on mobile devices, where computational resources are limited. In this paper we develop CondenseNet, a novel network architecture with unprecedented efficiency. It combines dense connectivity with a novel module called learned group convolution. The dense connectivity facilitat...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	85,358
1908.10522	Intuitive Neuromyoelectric Control of a Dexterous Bionic Arm Using a Modified Kalman Filter	Background: Multi-articulate prostheses are capable of performing dexterous hand movements. However, clinically available control strategies fail to provide users with intuitive, independent and proportional control over multiple degrees of freedom (DOFs) in real-time. New Method: We detail the use of a modified Kalman...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	143,136
1608.06098	On Preambles With Low Out of Band Radiation for Channel Estimation	In this paper, we investigate preamble designs for channel estimation, that jointly address the estimation efficiency in terms of MSE of the channel estimates, and the OOB radiation of the transmit preambles. We provide two novel design techniques, based on a convex optimization problem, to obtain optimal preambles for...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	60,072
1901.02975	A witness function based construction of discriminative models using Hermite polynomials	In machine learning, we are given a dataset of the form $\{(\mathbf{x}_j,y_j)\}_{j=1}^M$, drawn as i.i.d. samples from an unknown probability distribution $\mu$; the marginal distribution for the $\mathbf{x}_j$'s being $\mu^*$. We propose that rather than using a positive kernel such as the Gaussian for estimation of t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	118,313
1710.05386	Evolution of the Global Risk Network Mean-Field Stability Point	With a steadily growing human population and rapid advancements in technology, the global human network is increasing in size and connection density. This growth exacerbates networked global threats and can lead to unexpected consequences such as global epidemics mediated by air travel, threats in cyberspace, global go...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	82,635
1708.05448	On Ensuring that Intelligent Machines Are Well-Behaved	Machine learning algorithms are everywhere, ranging from simple data analysis and pattern recognition tools used across the sciences to complex systems that achieve super-human performance on various tasks. Ensuring that they are well-behaved---that they do not, for example, cause harm to humans or act in a racist or s...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	79,130
2202.09002	An Active and Contrastive Learning Framework for Fine-Grained Off-Road Semantic Segmentation	Off-road semantic segmentation with fine-grained labels is necessary for autonomous vehicles to understand driving scenes, as the coarse-grained road detection can not satisfy off-road vehicles with various mechanical properties. Fine-grained semantic segmentation in off-road scenes usually has no unified category defi...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	281,062
1710.04783	Retinal Vasculature Segmentation Using Local Saliency Maps and Generative Adversarial Networks For Image Super Resolution	We propose an image super resolution(ISR) method using generative adversarial networks (GANs) that takes a low resolution input fundus image and generates a high resolution super resolved (SR) image upto scaling factor of $16$. This facilitates more accurate automated image analysis, especially for small or blurred lan...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	82,531
2012.08011	DeepGamble: Towards unlocking real-time player intelligence using multi-layer instance segmentation and attribute detection	Annually the gaming industry spends approximately $15 billion in marketing reinvestment. However, this amount is spent without any consideration for the skill and luck of the player. For a casino, an unskilled player could fetch ~4 times more revenue than a skilled player. This paper describes a video recognition syste...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	211,626
1703.02854	Multiresolution Mapping and Informative Path Planning for UAV-based Terrain Monitoring	Unmanned aerial vehicles (UAVs) can offer timely and cost-effective delivery of high-quality sensing data. How- ever, deciding when and where to take measurements in complex environments remains an open challenge. To address this issue, we introduce a new multiresolution mapping approach for informative path planning i...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	69,629
2008.07372	Maximum Customers' Satisfaction in One-way Car-sharing: Modeling, Exact and Heuristic Solving	One-way car-sharing systems are transportation systems that allow customers to rent cars at stations scattered around the city, use them for a short journey, and return them at any station. The maximum customers' satisfaction problem concerns the task of assigning the cars, initially located at given stations, to maxim...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	192,093
2502.13017	Mean of Means: Human Localization with Calibration-free and Unconstrained Camera Settings (extended version)	Accurate human localization is crucial for various applications, especially in the Metaverse era. Existing high precision solutions rely on expensive, tag-dependent hardware, while vision-based methods offer a cheaper, tag-free alternative. However, current vision solutions based on stereo vision face limitations due t...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	535,166
2210.12437	Extractive Summarization of Legal Decisions using Multi-task Learning and Maximal Marginal Relevance	Summarizing legal decisions requires the expertise of law practitioners, which is both time- and cost-intensive. This paper presents techniques for extractive summarization of legal decisions in a low-resource setting using limited expert annotated data. We test a set of models that locate relevant content using a sequ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	325,742
1911.00364	Validation of a deep learning mammography model in a population with low screening rates	A key promise of AI applications in healthcare is in increasing access to quality medical care in under-served populations and emerging markets. However, deep learning models are often only trained on data from advantaged populations that have the infrastructure and resources required for large-scale data collection. I...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	151,802
1812.05484	Unsupervised Image Decomposition in Vector Layers	Deep image generation is becoming a tool to enhance artists and designers creativity potential. In this paper, we aim at making the generation process more structured and easier to interact with. Inspired by vector graphics systems, we propose a new deep image reconstruction paradigm where the outputs are composed from...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	116,423
2006.00578	"Judge me by my size (noun), do you?'' YodaLib: A Demographic-Aware Humor Generation Framework	The subjective nature of humor makes computerized humor generation a challenging task. We propose an automatic humor generation framework for filling the blanks in Mad Libs stories, while accounting for the demographic backgrounds of the desired audience. We collect a dataset consisting of such stories, which are fille...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	179,510
2311.06874	Distributed Charging Coordination of Electric Trucks with Limited Charging Resources	Electric trucks usually need to charge their batteries during long-range delivery missions, and the charging times are often nontrivial. As charging resources are limited, waiting times for some trucks can be prolonged at certain stations. To facilitate the efficient operation of electric trucks, we propose a distribut...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	407,112
2410.24050	Clustering Head: A Visual Case Study of the Training Dynamics in Transformers	This paper introduces the sparse modular addition task and examines how transformers learn it. We focus on transformers with embeddings in $\R^2$ and introduce a visual sandbox that provides comprehensive visualizations of each layer throughout the training process. We reveal a type of circuit, called "clustering heads...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	504,308
2003.02321	Approximating the Hotelling Observer with Autoencoder-Learned Efficient Channels for Binary Signal Detection Tasks	The objective assessment of image quality (IQ) has been advocated for the analysis and optimization of medical imaging systems. One method of obtaining such IQ metrics is through a mathematical observer. The Bayesian ideal observer is optimal by definition for signal detection tasks, but is frequently both intractable ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	166,906
2308.03081	Using Overlapping Methods to Counter Adversaries in Community Detection	When dealing with large graphs, community detection is a useful data triage tool that can identify subsets of the network that a data analyst should investigate. In an adversarial scenario, the graph may be manipulated to avoid scrutiny of certain nodes by the analyst. Robustness to such behavior is an important consid...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	383,890
2404.15592	ImplicitAVE: An Open-Source Dataset and Multimodal LLMs Benchmark for Implicit Attribute Value Extraction	Existing datasets for attribute value extraction (AVE) predominantly focus on explicit attribute values while neglecting the implicit ones, lack product images, are often not publicly available, and lack an in-depth human inspection across diverse domains. To address these limitations, we present ImplicitAVE, the first...	false	false	false	false	true	true	true	false	true	false	false	true	false	false	false	false	false	false	449,149
2405.11998	Learning to connect in action: Measuring and understanding the emergence of boundary spanners in volatile times	Collective intelligence of diverse groups is key for tackling many of today's grand challenges such as fostering resilience and climate adaptation. Information exchange across such diverse groups is crucial for collective intelligence, especially in volatile environments. To facilitate inter-group information exchange,...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	455,377
2411.16142	Causal Adjacency Learning for Spatiotemporal Prediction Over Graphs	Spatiotemporal prediction over graphs (STPG) is crucial for transportation systems. In existing STPG models, an adjacency matrix is an important component that captures the relations among nodes over graphs. However, most studies calculate the adjacency matrix by directly memorizing the data, such as distance- and corr...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	510,914
2210.03778	Optimal Gait Families using Lagrange Multiplier Method	The robotic locomotion community is interested in optimal gaits for control. Based on the optimization criterion, however, there could be a number of possible optimal gaits. For example, the optimal gait for maximizing displacement with respect to cost is quite different from the maximum displacement optimal gait. Beyo...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	322,168
2107.10161	Evidential Deep Learning for Open Set Action Recognition	In a real-world scenario, human actions are typically out of the distribution from training data, which requires a model to both recognize the known actions and reject the unknown. Different from image data, video actions are more challenging to be recognized in an open-set setting due to the uncertain temporal dynamic...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	247,230
2102.02557	Adaptive Semiparametric Language Models	We present a language model that combines a large parametric neural network (i.e., a transformer) with a non-parametric episodic memory component in an integrated architecture. Our model uses extended short-term context by caching local hidden states -- similar to transformer-XL -- and global long-term memory by retrie...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	218,450
2203.10929	General and Domain Adaptive Chinese Spelling Check with Error Consistent Pretraining	The lack of label data is one of the significant bottlenecks for Chinese Spelling Check (CSC). Existing researches use the method of automatic generation by exploiting unlabeled data to expand the supervised corpus. However, there is a big gap between the real input scenario and automatic generated corpus. Thus, we dev...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	286,737
2409.02632	Evaluating Environments Using Exploratory Agents	Exploration is a key part of many video games. We investigate the using an exploratory agent to provide feedback on the design of procedurally generated game levels, 5 engaging levels and 5 unengaging levels. We expand upon a framework introduced in previous research which models motivations for exploration and introdu...	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	485,772
2201.02848	Learning Sample Importance for Cross-Scenario Video Temporal Grounding	The task of temporal grounding aims to locate video moment in an untrimmed video, with a given sentence query. This paper for the first time investigates some superficial biases that are specific to the temporal grounding task, and proposes a novel targeted solution. Most alarmingly, we observe that existing temporal g...	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	274,675
2210.10670	Attaining Class-level Forgetting in Pretrained Model using Few Samples	In order to address real-world problems, deep learning models are jointly trained on many classes. However, in the future, some classes may become restricted due to privacy/ethical concerns, and the restricted class knowledge has to be removed from the models that have been trained on them. The available data may also ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	325,021
2502.04988	CMamba: Learned Image Compression with State Space Models	Learned Image Compression (LIC) has explored various architectures, such as Convolutional Neural Networks (CNNs) and transformers, in modeling image content distributions in order to achieve compression effectiveness. However, achieving high rate-distortion performance while maintaining low computational complexity (\i...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	531,391
1809.04556	Unsupervised Controllable Text Formalization	We propose a novel framework for controllable natural language transformation. Realizing that the requirement of parallel corpus is practically unsustainable for controllable generation tasks, an unsupervised training scheme is introduced. The crux of the framework is a deep neural encoder-decoder that is reinforced wi...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	107,598
1807.10268	Premise selection with neural networks and distributed representation of features	We present the problem of selecting relevant premises for a proof of a given statement. When stated as a binary classification task for pairs (conjecture, axiom), it can be efficiently solved using artificial neural networks. The key difference between our advance to solve this problem and previous approaches is the us...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	103,907
1312.1727	On the Capacity Region of Broadcast Packet Erasure Relay Networks With Feedback	We derive a new outer bound on the capacity region of broadcast traffic in multiple input broadcast packet erasure channels with feedback, and extend this outer bound to packet erasure relay networks with feedback. We show the tightness of the outer bound for various classes of networks. An important engineering implic...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	28,881
2303.11476	Chance-Constrained Multi-Robot Motion Planning under Gaussian Uncertainties	We consider a chance-constrained multi-robot motion planning problem in the presence of Gaussian motion and sensor noise. Our proposed algorithm, CC-K-CBS, leverages the scalability of kinodynamic conflict-based search (K-CBS) in conjunction with the efficiency of the Gaussian belief trees used in the Belief-A framewor...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	352,867
2407.04158	ELCC: the Emergent Language Corpus Collection	We introduce the Emergent Language Corpus Collection (ELCC): a collection of corpora generated from open source implementations of emergent communication systems across the literature. These systems include a variety of signalling game environments as well as more complex environments like a social deduction game and e...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	470,447
2210.17142	Towards Relation-centered Pooling and Convolution for Heterogeneous Graph Learning Networks	Heterogeneous graph neural network has unleashed great potential on graph representation learning and shown superior performance on downstream tasks such as node classification and clustering. Existing heterogeneous graph learning networks are primarily designed to either rely on pre-defined meta-paths or use attention...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	327,584
2105.14257	Diffusion-Based Representation Learning	Diffusion-based methods represented as stochastic differential equations on a continuous-time domain have recently proven successful as a non-adversarial generative model. Training such models relies on denoising score matching, which can be seen as multi-scale denoising autoencoders. Here, we augment the denoising sco...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	237,603
2403.03791	KG-TREAT: Pre-training for Treatment Effect Estimation by Synergizing Patient Data with Knowledge Graphs	Treatment effect estimation (TEE) is the task of determining the impact of various treatments on patient outcomes. Current TEE methods fall short due to reliance on limited labeled data and challenges posed by sparse and high-dimensional observational patient data. To address the challenges, we introduce a novel pre-tr...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	435,340
2209.10381	DARTSRepair: Core-failure-set Guided DARTS for Network Robustness to Common Corruptions	Network architecture search (NAS), in particular the differentiable architecture search (DARTS) method, has shown a great power to learn excellent model architectures on the specific dataset of interest. In contrast to using a fixed dataset, in this work, we focus on a different but important scenario for NAS: how to r...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	318,843

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