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
2102.07028	ThetA -- fast and robust clustering via a distance parameter	Clustering is a fundamental problem in machine learning where distance-based approaches have dominated the field for many decades. This set of problems is often tackled by partitioning the data into K clusters where the number of clusters is chosen apriori. While significant progress has been made on these lines over t...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	219,960
2207.00738	Golfer: Trajectory Prediction with Masked Goal Conditioning MnM Network	Transformers have enabled breakthroughs in NLP and computer vision, and have recently began to show promising performance in trajectory prediction for Autonomous Vehicle (AV). How to efficiently model the interactive relationships between the ego agent and other road and dynamic objects remains challenging for the stan...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	305,870
2109.12171	NICE: Robust Scheduling through Reinforcement Learning-Guided Integer Programming	Integer programs provide a powerful abstraction for representing a wide range of real-world scheduling problems. Despite their ability to model general scheduling problems, solving large-scale integer programs (IP) remains a computational challenge in practice. The incorporation of more complex objectives such as robus...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	257,182
2201.12664	A Deep CNN Architecture with Novel Pooling Layer Applied to Two Sudanese Arabic Sentiment Datasets	Arabic sentiment analysis has become an important research field in recent years. Initially, work focused on Modern Standard Arabic (MSA), which is the most widely-used form. Since then, work has been carried out on several different dialects, including Egyptian, Levantine and Moroccan. Moreover, a number of datasets h...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	277,730
2408.00332	Vision-based Wearable Steering Assistance for People with Impaired Vision in Jogging	Outdoor sports pose a challenge for people with impaired vision. The demand for higher-speed mobility inspired us to develop a vision-based wearable steering assistance. To ensure broad applicability, we focused on a representative sports environment, the athletics track. Our efforts centered on improving the speed and...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	477,798
1906.06717	Mo\"ET: Mixture of Expert Trees and its Application to Verifiable Reinforcement Learning	Rapid advancements in deep learning have led to many recent breakthroughs. While deep learning models achieve superior performance, often statistically better than humans, their adoption into safety-critical settings, such as healthcare or self-driving cars is hindered by their inability to provide safety guarantees or...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	135,391
2003.10687	Felix: Flexible Text Editing Through Tagging and Insertion	We present Felix --- a flexible text-editing approach for generation, designed to derive the maximum benefit from the ideas of decoding with bi-directional contexts and self-supervised pre-training. In contrast to conventional sequence-to-sequence (seq2seq) models, Felix is efficient in low-resource settings and fast a...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	169,413
2411.08072	Modeling variable guide efficiency in pooled CRISPR screens with ContrastiveVI+	Genetic screens mediated via CRISPR-Cas9 combined with high-content readouts have emerged as powerful tools for biological discovery. However, computational analyses of these screens come with additional challenges beyond those found with standard scRNA-seq analyses. For example, perturbation-induced variations of inte...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	507,768
1604.00691	Event excitation for event-driven control and optimization of multi-agent systems	We consider event-driven methods in a general framework for the control and optimization of multi-agent systems, viewing them as stochastic hybrid systems. Such systems often have feasible realizations in which the events needed to excite an on-line event-driven controller cannot occur, rendering the use of such contro...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	54,083
2501.00073	Position Information Emerges in Causal Transformers Without Positional Encodings via Similarity of Nearby Embeddings	Transformers with causal attention can solve tasks that require positional information without using positional encodings. In this work, we propose and investigate a new hypothesis about how positional information can be stored without using explicit positional encoding. We observe that nearby embeddings are more simil...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	521,528
2209.13090	EEG-based Image Feature Extraction for Visual Classification using Deep Learning	While capable of segregating visual data, humans take time to examine a single piece, let alone thousands or millions of samples. The deep learning models efficiently process sizeable information with the help of modern-day computing. However, their questionable decision-making process has raised considerable concerns....	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	319,764
2004.08202	Women worry about family, men about the economy: Gender differences in emotional responses to COVID-19	Among the critical challenges around the COVID-19 pandemic is dealing with the potentially detrimental effects on people's mental health. Designing appropriate interventions and identifying the concerns of those most at risk requires methods that can extract worries, concerns and emotional responses from text data. We ...	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	false	false	false	173,000
2307.00758	Structured Network Pruning by Measuring Filter-wise Interactions	Structured network pruning is a practical approach to reduce computation cost directly while retaining the CNNs' generalization performance in real applications. However, identifying redundant filters is a core problem in structured network pruning, and current redundancy criteria only focus on individual filters' attr...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	377,119
2101.00688	Segmentation and genome annotation algorithms	Segmentation and genome annotation (SAGA) algorithms are widely used to understand genome activity and gene regulation. These algorithms take as input epigenomic datasets, such as chromatin immunoprecipitation-sequencing (ChIP-seq) measurements of histone modifications or transcription factor binding. They partition th...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	214,166
2112.00236	VoRTX: Volumetric 3D Reconstruction With Transformers for Voxelwise View Selection and Fusion	Recent volumetric 3D reconstruction methods can produce very accurate results, with plausible geometry even for unobserved surfaces. However, they face an undesirable trade-off when it comes to multi-view fusion. They can fuse all available view information by global averaging, thus losing fine detail, or they can heur...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	269,066
1207.1563	Achievable Sum-Rates in Gaussian Multiple-Access Channels with MIMO-AF-Relay and Direct Links	We consider a single-antenna Gaussian multiple-access channel (MAC) with a multiple-antenna amplify-and-forward (AF) relay, where, contrary to many previous works, also the direct links between transmitters and receiver are taken into account. For this channel, we investigate two transmit schemes: Sending and relaying ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	17,322
1811.07662	Intention Oriented Image Captions with Guiding Objects	Although existing image caption models can produce promising results using recurrent neural networks (RNNs), it is difficult to guarantee that an object we care about is contained in generated descriptions, for example in the case that the object is inconspicuous in the image. Problems become even harder when these obj...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	113,839
1906.00431	An Empirical Study on Hyperparameters and their Interdependence for RL Generalization	Recent results in Reinforcement Learning (RL) have shown that agents with limited training environments are susceptible to a large amount of overfitting across many domains. A key challenge for RL generalization is to quantitatively explain the effects of changing parameters on testing performance. Such parameters incl...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	133,396
2310.06233	Low-Rank Tensor Completion via Novel Sparsity-Inducing Regularizers	To alleviate the bias generated by the l1-norm in the low-rank tensor completion problem, nonconvex surrogates/regularizers have been suggested to replace the tensor nuclear norm, although both can achieve sparsity. However, the thresholding functions of these nonconvex regularizers may not have closed-form expressions...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	398,484
2210.03974	FBNet: Feedback Network for Point Cloud Completion	The rapid development of point cloud learning has driven point cloud completion into a new era. However, the information flows of most existing completion methods are solely feedforward, and high-level information is rarely reused to improve low-level feature learning. To this end, we propose a novel Feedback Network (...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	322,255
1506.04897	Parsing Natural Language Sentences by Semi-supervised Methods	We present our work on semi-supervised parsing of natural language sentences, focusing on multi-source crosslingual transfer of delexicalized dependency parsers. We first evaluate the influence of treebank annotation styles on parsing performance, focusing on adposition attachment style. Then, we present KLcpos3, an em...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	44,231
2011.09580	Non-Linear Multiple Field Interactions Neural Document Ranking	Ranking tasks are usually based on the text of the main body of the page and the actions (clicks) of users on the page. There are other elements that could be leveraged to better contextualise the ranking experience (e.g. text in other fields, query made by the user, images, etc). We present one of the first in-depth a...	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	207,229
1407.3556	Optimal Spectrum Management in Two-User Interference Channels	In this work, we address the problem of optimal spectrum management in continuous frequency domain in multiuser interference channels. The objective is to maximize the weighted sum of user capacities. Our main results are as follows: (i) For frequency-selective channels, we prove that in an optimal solution, each user ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	34,633
2006.15222	BERTology Meets Biology: Interpreting Attention in Protein Language Models	Transformer architectures have proven to learn useful representations for protein classification and generation tasks. However, these representations present challenges in interpretability. In this work, we demonstrate a set of methods for analyzing protein Transformer models through the lens of attention. We show that...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	184,445
2209.01301	Geometry of EM and related iterative algorithms	The Expectation--Maximization (EM) algorithm is a simple meta-algorithm that has been used for many years as a methodology for statistical inference when there are missing measurements in the observed data or when the data is composed of observables and unobservables. Its general properties are well studied, and also, ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	315,829
2406.18333	Continuous Sign Language Recognition Using Intra-inter Gloss Attention	Many continuous sign language recognition (CSLR) studies adopt transformer-based architectures for sequence modeling due to their powerful capacity for capturing global contexts. Nevertheless, vanilla self-attention, which serves as the core module of the transformer, calculates a weighted average over all time steps; ...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	467,970
1903.09372	Few-shot Adaptive Faster R-CNN	To mitigate the detection performance drop caused by domain shift, we aim to develop a novel few-shot adaptation approach that requires only a few target domain images with limited bounding box annotations. To this end, we first observe several significant challenges. First, the target domain data is highly insufficien...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	125,048
2210.09220	A Saccaded Visual Transformer for General Object Spotting	This paper presents the novel combination of a visual transformer style patch classifier with saccaded local attention. A novel optimisation paradigm for training object models is also presented, rather than the optimisation function minimising class membership probability error the network is trained to estimate the n...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	324,458
2205.12013	Naive Few-Shot Learning: Uncovering the fluid intelligence of machines	In this paper, we aimed to help bridge the gap between human fluid intelligence - the ability to solve novel tasks without prior training - and the performance of deep neural networks, which typically require extensive prior training. An essential cognitive component for solving intelligence tests, which in humans are ...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	true	false	false	298,375
2006.04232	Tensors over Semirings for Latent-Variable Weighted Logic Programs	Semiring parsing is an elegant framework for describing parsers by using semiring weighted logic programs. In this paper we present a generalization of this concept: latent-variable semiring parsing. With our framework, any semiring weighted logic program can be latentified by transforming weights from scalar values of...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	180,617
1704.04920	Deep Joint Entity Disambiguation with Local Neural Attention	We propose a novel deep learning model for joint document-level entity disambiguation, which leverages learned neural representations. Key components are entity embeddings, a neural attention mechanism over local context windows, and a differentiable joint inference stage for disambiguation. Our approach thereby combin...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	71,917
2210.03899	Multi-Scale Wavelet Transformer for Face Forgery Detection	Currently, many face forgery detection methods aggregate spatial and frequency features to enhance the generalization ability and gain promising performance under the cross-dataset scenario. However, these methods only leverage one level frequency information which limits their expressive ability. To overcome these lim...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	322,216
2312.03779	Public emotions on Internet: In case of AIGC	The proliferation of interactive AI like ChatGPT has fueled intense public discourse surrounding AI- generated content (AIGC). While some fear job displacement, others anticipate productivity gains. Social media provides a rich source of data reflecting public opinion, attitudes, and behaviors. By examining the factors...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	413,425
2110.13373	EnTRPO: Trust Region Policy Optimization Method with Entropy Regularization	Trust Region Policy Optimization (TRPO) is a popular and empirically successful policy search algorithm in reinforcement learning (RL). It iteratively solved the surrogate problem which restricts consecutive policies to be close to each other. TRPO is an on-policy algorithm. On-policy methods bring many benefits, like ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	263,153
2410.15794	Habaek: High-performance water segmentation through dataset expansion and inductive bias optimization	Water segmentation is critical to disaster response and water resource management. Authorities may employ high-resolution photography to monitor rivers, lakes, and reservoirs, allowing for more proactive management in agriculture, industry, and conservation. Deep learning has improved flood monitoring by allowing model...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	500,729
1709.05065	Asian Stamps Identification and Classification System	In this paper, we address the problem of stamp recognition. The goal is to classify a given stamp to a certain country and also identify the year it is published. We propose a new approach for stamp recognition based on describing a given stamp image using color information and texture information. For color informatio...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	80,776
2210.10703	AUC-based Selective Classification	Selective classification (or classification with a reject option) pairs a classifier with a selection function to determine whether or not a prediction should be accepted. This framework trades off coverage (probability of accepting a prediction) with predictive performance, typically measured by distributive loss func...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	325,032
1907.12430	Learning abstract perceptual notions: the example of space	Humans are extremely swift learners. We are able to grasp highly abstract notions, whether they come from art perception or pure mathematics. Current machine learning techniques demonstrate astonishing results in extracting patterns in information. Yet the abstract notions we possess are more than just statistical patt...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	140,126
1911.05371	Self-labelling via simultaneous clustering and representation learning	Combining clustering and representation learning is one of the most promising approaches for unsupervised learning of deep neural networks. However, doing so naively leads to ill posed learning problems with degenerate solutions. In this paper, we propose a novel and principled learning formulation that addresses these...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	true	false	false	153,242
1902.07114	Sequential Synthesis of Distributed Controllers for Cascade Interconnected Systems	We consider the problem of designing distributed controllers to ensure passivity of a large-scale interconnection of linear subsystems connected in a cascade topology. The control design process needs to be carried out at the subsystem-level with no direct knowledge of the dynamics of other subsystems in the interconne...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	121,913
2409.17073	Enhancing Post-Hoc Attributions in Long Document Comprehension via Coarse Grained Answer Decomposition	Accurately attributing answer text to its source document is crucial for developing a reliable question-answering system. However, attribution for long documents remains largely unexplored. Post-hoc attribution systems are designed to map answer text back to the source document, yet the granularity of this mapping has ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	491,637
2212.05397	Task modules Partitioning, Scheduling and Floorplanning for Partially Dynamically Reconfigurable Systems Based on Modern Heterogeneous FPGAs	Modern field programmable gate array(FPGA) can be partially dynamically reconfigurable with heterogeneous resources distributed on the chip. And FPGA-based partially dynamically reconfigurable system(FPGA-PDRS) can be used to accelerate computing and improve computing flexibility. However, the traditional design of F...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	335,776
2401.16497	A Bayesian Gaussian Process-Based Latent Discriminative Generative Decoder (LDGD) Model for High-Dimensional Data	Extracting meaningful information from high-dimensional data poses a formidable modeling challenge, particularly when the data is obscured by noise or represented through different modalities. This research proposes a novel non-parametric modeling approach, leveraging the Gaussian process (GP), to characterize high-dim...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	424,856
1203.3536	A Convex Formulation for Learning Task Relationships in Multi-Task Learning	Multi-task learning is a learning paradigm which seeks to improve the generalization performance of a learning task with the help of some other related tasks. In this paper, we propose a regularization formulation for learning the relationships between tasks in multi-task learning. This formulation can be viewed as a n...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	14,984
1605.00286	Multidimensional Scaling on Multiple Input Distance Matrices	Multidimensional Scaling (MDS) is a classic technique that seeks vectorial representations for data points, given the pairwise distances between them. However, in recent years, data are usually collected from diverse sources or have multiple heterogeneous representations. How to do multidimensional scaling on multiple ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	55,321
2002.01284	Obstruction level detection of sewer videos using convolutional neural networks	Worldwide, sewer networks are designed to transport wastewater to a centralized treatment plant to be treated and returned to the environment. This process is critical for the current society, preventing waterborne illnesses, providing safe drinking water and enhancing general sanitation. To keep a sewer network perfec...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	162,621
2309.08054	Permutation Capacity Region of Adder Multiple-Access Channels	Point-to-point permutation channels are useful models of communication networks and biological storage mechanisms and have received theoretical attention in recent years. Propelled by relevant advances in this area, we analyze the permutation adder multiple-access channel (PAMAC) in this work. In the PAMAC network mode...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	392,020
2301.06047	EvoAAA: An evolutionary methodology for automated \neural autoencoder architecture search	Machine learning models work better when curated features are provided to them. Feature engineering methods have been usually used as a preprocessing step to obtain or build a proper feature set. In late years, autoencoders (a specific type of symmetrical neural network) have been widely used to perform representation ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	true	false	false	340,531
2108.05338	Truncated Emphatic Temporal Difference Methods for Prediction and Control	Emphatic Temporal Difference (TD) methods are a class of off-policy Reinforcement Learning (RL) methods involving the use of followon traces. Despite the theoretical success of emphatic TD methods in addressing the notorious deadly triad of off-policy RL, there are still two open problems. First, followon traces typica...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	250,277
2307.05584	Code Generation for Machine Learning using Model-Driven Engineering and SysML	Data-driven engineering refers to systematic data collection and processing using machine learning to improve engineering systems. Currently, the implementation of data-driven engineering relies on fundamental data science and software engineering skills. At the same time, model-based engineering is gaining relevance f...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	378,792
1812.04783	Deep Air Quality Forecasting Using Hybrid Deep Learning Framework	Air quality forecasting has been regarded as the key problem of air pollution early warning and control management. In this paper, we propose a novel deep learning model for air quality (mainly PM2.5) forecasting, which learns the spatial-temporal correlation features and interdependence of multivariate air quality rel...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	116,280
2307.07227	Secure Short-Packet Communications via UAV-Enabled Mobile Relaying: Joint Resource Optimization and 3D Trajectory Design	Short-packet communication (SPC) and unmanned aerial vehicles (UAVs) are anticipated to play crucial roles in the development of 5G-and-beyond wireless networks and the Internet of Things (IoT). In this paper, we propose a secure SPC system, where a UAV serves as a mobile decode-and-forward (DF) relay, periodically rec...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	379,325
1109.6884	ERA: Efficient Serial and Parallel Suffix Tree Construction for Very Long Strings	The suffix tree is a data structure for indexing strings. It is used in a variety of applications such as bioinformatics, time series analysis, clustering, text editing and data compression. However, when the string and the resulting suffix tree are too large to fit into the main memory, most existing construction algo...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	12,423
2405.18498	The Unified Balance Theory of Second-Moment Exponential Scaling Optimizers in Visual Tasks	We have identified a potential method for unifying first-order optimizers through the use of variable Second-Moment Exponential Scaling(SMES). We begin with back propagation, addressing classic phenomena such as gradient vanishing and explosion, as well as issues related to dataset sparsity, and introduce the theory of...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	458,447
2202.05014	Coverage Probability and Spectral Efficiency Analysis of Multi-Gateway Downlink LoRa Networks	The system-level performance of multi-gateway downlink long-range (LoRa) networks is investigated in the present paper. Specifically, we first compute the active probability of a channel and the selection probability of an active end-device (ED) in the closed-form expressions. We then derive the coverage probability ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	279,743
2412.12656	DriveTester: A Unified Platform for Simulation-Based Autonomous Driving Testing	Simulation-based testing plays a critical role in evaluating the safety and reliability of autonomous driving systems (ADSs). However, one of the key challenges in ADS testing is the complexity of preparing and configuring simulation environments, particularly in terms of compatibility and stability between the simulat...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	517,970
1806.11311	Guaranteed Deterministic Bounds on the Total Variation Distance between Univariate Mixtures	The total variation distance is a core statistical distance between probability measures that satisfies the metric axioms, with value always falling in $[0,1]$. This distance plays a fundamental role in machine learning and signal processing: It is a member of the broader class of $f$-divergences, and it is related to ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	101,689
2407.14967	Base and Exponent Prediction in Mathematical Expressions using Multi-Output CNN	The use of neural networks and deep learning techniques in image processing has significantly advanced the field, enabling highly accurate recognition results. However, achieving high recognition rates often necessitates complex network models, which can be challenging to train and require substantial computational res...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	474,975
1704.01984	A Delay-Aware Caching Algorithm for Wireless D2D Caching Networks	Recently, wireless caching techniques have been studied to satisfy lower delay requirements and offload traffic from peak periods. By storing parts of the popular files at the mobile users, users can locate some of their requested files in their own caches or the caches at their neighbors. In the latter case, when a us...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	71,357
2108.03854	Coordination on Time-Varying Antagonistic Networks	This paper studies coordination problem for time-varying networks suffering from antagonistic information, quantified by scaling parameters. By such a manner, interacting property of the participating individuals and antagonistic information can be quantified in a fully decoupled perspective, thus benefiting from merel...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	249,801
2409.18832	Classification and regression of trajectories rendered as images via 2D Convolutional Neural Networks	Trajectories can be regarded as time-series of coordinates, typically arising from motile objects. Methods for trajectory classification are particularly important to detect different movement patterns, while methods for regression to compute motility metrics and forecasting. Recent advances in computer vision have fac...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	492,424
2312.14646	Collaborative Synthesis of Patient Records through Multi-Visit Health State Inference	Electronic health records (EHRs) have become the foundation of machine learning applications in healthcare, while the utility of real patient records is often limited by privacy and security concerns. Synthetic EHR generation provides an additional perspective to compensate for this limitation. Most existing methods sy...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	417,710
2206.03996	Sharp-MAML: Sharpness-Aware Model-Agnostic Meta Learning	Model-agnostic meta learning (MAML) is currently one of the dominating approaches for few-shot meta-learning. Albeit its effectiveness, the optimization of MAML can be challenging due to the innate bilevel problem structure. Specifically, the loss landscape of MAML is much more complex with possibly more saddle points ...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	301,479
2208.09439	Adapting Task-Oriented Dialogue Models for Email Conversations	Intent detection is a key part of any Natural Language Understanding (NLU) system of a conversational assistant. Detecting the correct intent is essential yet difficult for email conversations where multiple directives and intents are present. In such settings, conversation context can become a key disambiguating facto...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	313,702
2409.00617	Does Knowledge Localization Hold True? Surprising Differences Between Entity and Relation Perspectives in Language Models	Large language models encapsulate knowledge and have demonstrated superior performance on various natural language processing tasks. Recent studies have localized this knowledge to specific model parameters, such as the MLP weights in intermediate layers. This study investigates the differences between entity and relat...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	484,999
1911.09887	UAV-enabled Secure Communication with Finite Blocklength	In the finite blocklength scenario, which is suitable for practical applications, a method of maximizing the average effective secrecy rate (AESR) is proposed for a UAV-enabled secure communication by optimizing the UAV's trajectory and transmit power subject to the UAV's mobility constraints and transmit power constra...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	154,659
2302.14383	Linear Spaces of Meanings: Compositional Structures in Vision-Language Models	We investigate compositional structures in data embeddings from pre-trained vision-language models (VLMs). Traditionally, compositionality has been associated with algebraic operations on embeddings of words from a pre-existing vocabulary. In contrast, we seek to approximate representations from an encoder as combinati...	false	false	false	false	true	false	true	false	true	false	false	true	false	false	false	false	false	false	348,273
2402.09821	Diffusion Models for Audio Restoration	With the development of audio playback devices and fast data transmission, the demand for high sound quality is rising for both entertainment and communications. In this quest for better sound quality, challenges emerge from distortions and interferences originating at the recording side or caused by an imperfect trans...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	429,698
2205.07104	Evolutionary optimization of the Verlet closure relation for the hard-sphere and square-well fluids	The Ornstein-Zernike equation is solved for the hard-sphere and square-well fluids using a diverse selection of closure relations; the attraction range of the square-well is chosen to be $\lambda=1.5.$ In particular, for both fluids we mainly focus on the solution based on a three-parameter version of the Verlet closur...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	296,479
2403.11146	Toward Adaptive Cooperation: Model-Based Shared Control Using LQ-Differential Games	This paper introduces a novel model-based adaptive shared control to allow for the identification and design challenge for shared-control systems, in which humans and automation share control tasks. The main challenge is the adaptive behavior of the human in such shared control interactions. Consequently, merely identi...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	438,563
1408.3967	Learning Deep Representation for Face Alignment with Auxiliary Attributes	In this study, we show that landmark detection or face alignment task is not a single and independent problem. Instead, its robustness can be greatly improved with auxiliary information. Specifically, we jointly optimize landmark detection together with the recognition of heterogeneous but subtly correlated facial attr...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	35,430
2401.18032	DROP: Decouple Re-Identification and Human Parsing with Task-specific Features for Occluded Person Re-identification	The paper introduces the Decouple Re-identificatiOn and human Parsing (DROP) method for occluded person re-identification (ReID). Unlike mainstream approaches using global features for simultaneous multi-task learning of ReID and human parsing, or relying on semantic information for attention guidance, DROP argues that...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	425,404
1706.04626	Estimation and Mitigation of Channel Non-Reciprocity in Massive MIMO	Time-division duplex (TDD) based massive MIMO systems rely on the reciprocity of the wireless propagation channels when calculating the downlink precoders based on uplink pilots. However, the effective uplink and downlink channels incorporating the analog radio front-ends of the base station (BS) and user equipments (U...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	75,368
2307.02144	Kolam Simulation using Angles at Lattice Points	Kolam is a ritual art form practised by people in South India and consists of rule-bound geometric patterns of dots and lines. Single loop Kolams are mathematical closed loop patterns drawn over a grid of dots and conforming to certain heuristics. In this work, we propose a novel encoding scheme where we map the angula...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	377,601
1508.07680	Domain Generalization for Object Recognition with Multi-task Autoencoders	The problem of domain generalization is to take knowledge acquired from a number of related domains where training data is available, and to then successfully apply it to previously unseen domains. We propose a new feature learning algorithm, Multi-Task Autoencoder (MTAE), that provides good generalization performance ...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	46,427
1804.03312	Crafting a Toolchain for Image Restoration by Deep Reinforcement Learning	We investigate a novel approach for image restoration by reinforcement learning. Unlike existing studies that mostly train a single large network for a specialized task, we prepare a toolbox consisting of small-scale convolutional networks of different complexities and specialized in different tasks. Our method, RL-Res...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	94,601
1902.01117	Exploring Temporal Dependencies in Multimodal Referring Expressions with Mixed Reality	In collaborative tasks, people rely both on verbal and non-verbal cues simultaneously to communicate with each other. For human-robot interaction to run smoothly and naturally, a robot should be equipped with the ability to robustly disambiguate referring expressions. In this work, we propose a model that can disambigu...	true	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	120,594
1901.02565	High-Fidelity Vector Space Models of Structured Data	Machine learning systems regularly deal with structured data in real-world applications. Unfortunately, such data has been difficult to faithfully represent in a way that most machine learning techniques would expect, i.e. as a real-valued vector of a fixed, pre-specified size. In this work, we introduce a novel approa...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	118,237
2401.01978	Tailor: Size Recommendations for High-End Fashion Marketplaces	In the ever-changing and dynamic realm of high-end fashion marketplaces, providing accurate and personalized size recommendations has become a critical aspect. Meeting customer expectations in this regard is not only crucial for ensuring their satisfaction but also plays a pivotal role in driving customer retention, wh...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	419,553
2112.00094	Leveraging Intrinsic Gradient Information for Further Training of Differentiable Machine Learning Models	Designing models that produce accurate predictions is the fundamental objective of machine learning (ML). This work presents methods demonstrating that when the derivatives of target variables (outputs) with respect to inputs can be extracted from processes of interest, e.g., neural networks (NN) based surrogate models...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	269,029
2412.07039	Data Augmentation with Variational Autoencoder for Imbalanced Dataset	Learning from an imbalanced distribution presents a major challenge in predictive modeling, as it generally leads to a reduction in the performance of standard algorithms. Various approaches exist to address this issue, but many of them concern classification problems, with a limited focus on regression. In this paper,...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	515,482
2002.09023	Audio-video Emotion Recognition in the Wild using Deep Hybrid Networks	This paper presents an audiovisual-based emotion recognition hybrid network. While most of the previous work focuses either on using deep models or hand-engineered features extracted from images, we explore multiple deep models built on both images and audio signals. Specifically, in addition to convolutional neural ne...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	164,933
2005.13629	Simultaneous Diagonalization of Incomplete Matrices and Applications	We consider the problem of recovering the entries of diagonal matrices $\{U_a\}_a$ for $a = 1,\ldots,t$ from multiple "incomplete" samples $\{W_a\}_a$ of the form $W_a=PU_aQ$, where $P$ and $Q$ are unknown matrices of low rank. We devise practical algorithms for this problem depending on the ranks of $P$ and $Q$. This ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	179,057
1705.06530	Fake it till you make it: Fishing for Catfishes	Many adult content websites incorporate social networking features. Although these are popular, they raise significant challenges, including the potential for users to "catfish", i.e., to create fake profiles to deceive other users. This paper takes an initial step towards automated catfish detection. We explore the ch...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	73,648
2208.12856	Local Context-Aware Active Domain Adaptation	Active Domain Adaptation (ADA) queries the labels of a small number of selected target samples to help adapting a model from a source domain to a target domain. The local context of queried data is important, especially when the domain gap is large. However, this has not been fully explored by existing ADA works. In th...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	314,873
2402.16124	AVI-Talking: Learning Audio-Visual Instructions for Expressive 3D Talking Face Generation	While considerable progress has been made in achieving accurate lip synchronization for 3D speech-driven talking face generation, the task of incorporating expressive facial detail synthesis aligned with the speaker's speaking status remains challenging. Our goal is to directly leverage the inherent style information c...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	432,435
2410.19512	Marked Temporal Bayesian Flow Point Processes	Marked event data captures events by recording their continuous-valued occurrence timestamps along with their corresponding discrete-valued types. They have appeared in various real-world scenarios such as social media, financial transactions, and healthcare records, and have been effectively modeled through Marked Tem...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	502,348
2307.05131	Overview of BioASQ 2023: The eleventh BioASQ challenge on Large-Scale Biomedical Semantic Indexing and Question Answering	This is an overview of the eleventh edition of the BioASQ challenge in the context of the Conference and Labs of the Evaluation Forum (CLEF) 2023. BioASQ is a series of international challenges promoting advances in large-scale biomedical semantic indexing and question answering. This year, BioASQ consisted of new edit...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	378,634
1806.05005	Proactive Resource Allocation with Predictable Channel Statistics	The behavior of users in relatively predictable, both in terms of the data they request and the wireless channels they observe. In this paper, we consider the statistics of such predictable patterns of the demand and channel jointly across multiple users, and develop a novel predictive resource allocation method. This ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	100,367
2210.16898	Attention Swin U-Net: Cross-Contextual Attention Mechanism for Skin Lesion Segmentation	Melanoma is caused by the abnormal growth of melanocytes in human skin. Like other cancers, this life-threatening skin cancer can be treated with early diagnosis. To support a diagnosis by automatic skin lesion segmentation, several Fully Convolutional Network (FCN) approaches, specifically the U-Net architecture, have...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	327,492
2305.18623	Alfred: A System for Prompted Weak Supervision	Alfred is the first system for programmatic weak supervision (PWS) that creates training data for machine learning by prompting. In contrast to typical PWS systems where weak supervision sources are programs coded by experts, Alfred enables users to encode their subject matter expertise via natural language prompts for...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	369,166
1512.04280	Small-footprint Deep Neural Networks with Highway Connections for Speech Recognition	For speech recognition, deep neural networks (DNNs) have significantly improved the recognition accuracy in most of benchmark datasets and application domains. However, compared to the conventional Gaussian mixture models, DNN-based acoustic models usually have much larger number of model parameters, making it challeng...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	true	false	false	50,120
2012.06791	Bayesian graph neural networks for strain-based crack localization	A common shortcoming of vibration-based damage localization techniques is that localized damages, i.e. small cracks, have a limited influence on the spectral characteristics of a structure. In contrast, even the smallest of defects, under particular loading conditions, cause localized strain concentrations with predict...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	211,227
1810.08878	A Regressive Convolution Neural network and Support Vector Regression Model for Electricity Consumption Forecasting	Electricity consumption forecasting has important implications for the mineral companies on guiding quarterly work, normal power system operation, and the management. However, electricity consumption prediction for the mineral company is different from traditional electricity load prediction since mineral company elect...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	110,934
1308.0586	A note on norm-based Lyapunov functions via contraction analysis	It is well know that for globally contractive autonomous systems, there exists a unique equilibrium and the distance to the equilibrium evaluated along any trajectory decreases exponentially with time. We show that, additionally, the magnitude of the velocity evaluated along any trajectory decreases exponentially, thus...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	26,235
1809.07725	Specimens as research objects: reconciliation across distributed repositories to enable metadata propagation	Botanical specimens are shared as long-term consultable research objects in a global network of specimen repositories. Multiple specimens are generated from a shared field collection event; generated specimens are then managed individually in separate repositories and independently augmented with research and managemen...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	108,343
2310.13240	Transparency challenges in policy evaluation with causal machine learning -- improving usability and accountability	Causal machine learning tools are beginning to see use in real-world policy evaluation tasks to flexibly estimate treatment effects. One issue with these methods is that the machine learning models used are generally black boxes, i.e., there is no globally interpretable way to understand how a model makes estimates. Th...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	401,352
2501.13188	Topological constraints on self-organisation in locally interacting systems	All intelligence is collective intelligence, in the sense that it is made of parts which must align with respect to system-level goals. Understanding the dynamics which facilitate or limit navigation of problem spaces by aligned parts thus impacts many fields ranging across life sciences and engineering. To that end, c...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	526,584
2502.04266	Digital Gatekeeping: An Audit of Search Engine Results shows tailoring of queries on the Israel-Palestine Conflict	Search engines, often viewed as reliable gateways to information, tailor search results using customization algorithms based on user preferences, location, and more. While this can be useful for routine queries, it raises concerns when the topics are sensitive or contentious, possibly limiting exposure to diverse viewp...	false	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	531,045
1909.11850	Improved Lower Bounds for Pliable Index Coding using Absent Receivers	This paper studies pliable index coding, in which a sender broadcasts information to multiple receivers through a shared broadcast medium, and the receivers each have some message a priori and want any message they do not have. An approach, based on receivers that are absent from the problem, was previously proposed to...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	146,938

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