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
1308.4067	The S-metric, the Beichl-Cloteaux approximation, and preferential attachment	The S-metric has grown popular in network studies, as a measure of ``scale-freeness'' restricted to the collection G(D) of connected graphs with a common degree sequence D=(d_1,\ldots,d_n). The calculation of S depends on the maximum possible degree assortativity r among graphs in G(D). The original method involves a h...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	26,526
2411.02397	Adaptive Caching for Faster Video Generation with Diffusion Transformers	Generating temporally-consistent high-fidelity videos can be computationally expensive, especially over longer temporal spans. More-recent Diffusion Transformers (DiTs) -- despite making significant headway in this context -- have only heightened such challenges as they rely on larger models and heavier attention mecha...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	505,470
1308.6537	Percolation on random networks with arbitrary k-core structure	The k-core decomposition of a network has thus far mainly served as a powerful tool for the empirical study of complex networks. We now propose its explicit integration in a theoretical model. We introduce a Hard-core Random Network model that generates maximally random networks with arbitrary degree distribution and a...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	26,723
1812.01077	Brief survey of Mobility Analyses based on Mobile Phone Datasets	This is a brief survey of the research performed by Grandata Labs in collaboration with numerous academic groups around the world on the topic of human mobility. A driving theme in these projects is to use and improve Data Science techniques to understand mobility, as it can be observed through the lens of mobile phone...	false	false	false	true	false	false	true	false	false	false	false	false	false	true	false	false	false	false	115,421
1607.07015	Fronthauling for 5G LTE-U Ultra Dense Cloud Small Cell Networks	Ultra dense cloud small cell network (UDCSNet), which combines cloud computing and massive deployment of small cells, is a promising technology for the fifth-generation (5G) LTE-U mobile communications because it can accommodate the anticipated explosive growth of mobile users' data traffic. As a result, fronthauling b...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	58,966
2409.06433	Fine-tuning and Prompt Engineering with Cognitive Knowledge Graphs for Scholarly Knowledge Organization	The increasing amount of published scholarly articles, exceeding 2.5 million yearly, raises the challenge for researchers in following scientific progress. Integrating the contributions from scholarly articles into a novel type of cognitive knowledge graph (CKG) will be a crucial element for accessing and organizing sc...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	true	487,124
2501.15590	Assessing and Predicting Air Pollution in Asia: A Regional and Temporal Study (2018-2023)	This study analyzes and predicts air pollution in Asia, focusing on PM 2.5 levels from 2018 to 2023 across five regions: Central, East, South, Southeast, and West Asia. South Asia emerged as the most polluted region, with Bangladesh, India, and Pakistan consistently having the highest PM 2.5 levels and death rates, esp...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	527,625
2406.11248	Performance Improvement of Language-Queried Audio Source Separation Based on Caption Augmentation From Large Language Models for DCASE Challenge 2024 Task 9	We present a prompt-engineering-based text-augmentation approach applied to a language-queried audio source separation (LASS) task. To enhance the performance of LASS, the proposed approach utilizes large language models (LLMs) to generate multiple captions corresponding to each sentence of the training dataset. To thi...	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	464,793
2103.15620	Asymptotically Optimal Massey-Like Inequality on Guessing Entropy With Application to Side-Channel Attack Evaluations	A Massey-like inequality is any useful lower bound on guessing entropy in terms of the computationally scalable Shannon entropy. The asymptotically optimal Massey-like inequality is determined and further refined for finite-support distributions. The impact of these results are highlighted for side-channel attack evalu...	false	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	227,287
2410.10589	MoTE: Reconciling Generalization with Specialization for Visual-Language to Video Knowledge Transfer	Transferring visual-language knowledge from large-scale foundation models for video recognition has proved to be effective. To bridge the domain gap, additional parametric modules are added to capture the temporal information. However, zero-shot generalization diminishes with the increase in the number of specialized p...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	498,148
1712.00386	Probabilistic Adaptive Computation Time	We present a probabilistic model with discrete latent variables that control the computation time in deep learning models such as ResNets and LSTMs. A prior on the latent variables expresses the preference for faster computation. The amount of computation for an input is determined via amortized maximum a posteriori (M...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	85,889
2110.06357	Tangent Space and Dimension Estimation with the Wasserstein Distance	Consider a set of points sampled independently near a smooth compact submanifold of Euclidean space. We provide mathematically rigorous bounds on the number of sample points required to estimate both the dimension and the tangent spaces of that manifold with high confidence. The algorithm for this estimation is Local P...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	260,577
2310.08847	On the Over-Memorization During Natural, Robust and Catastrophic Overfitting	Overfitting negatively impacts the generalization ability of deep neural networks (DNNs) in both natural and adversarial training. Existing methods struggle to consistently address different types of overfitting, typically designing strategies that focus separately on either natural or adversarial patterns. In this wor...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	399,561
2212.05590	PromptCAL: Contrastive Affinity Learning via Auxiliary Prompts for Generalized Novel Category Discovery	Although existing semi-supervised learning models achieve remarkable success in learning with unannotated in-distribution data, they mostly fail to learn on unlabeled data sampled from novel semantic classes due to their closed-set assumption. In this work, we target a pragmatic but under-explored Generalized Novel Cat...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	335,827
0901.0643	An Information Theoretic Analysis of Single Transceiver Passive RFID Networks	In this paper, we study single transceiver passive RFID networks by modeling the underlying physical system as a special cascade of a certain broadcast channel (BCC) and a multiple access channel (MAC), using a "nested codebook" structure in between. The particular application differentiates this communication setup fr...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	2,897
2401.00023	CycleGAN Models for MRI Image Translation	Image-to-image translation has gained popularity in the medical field to transform images from one domain to another. Medical image synthesis via domain transformation is advantageous in its ability to augment an image dataset where images for a given class is limited. From the learning perspective, this process contri...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	418,853
2305.14549	Extracting Shopping Interest-Related Product Types from the Web	Recommending a diversity of product types (PTs) is important for a good shopping experience when customers are looking for products around their high-level shopping interests (SIs) such as hiking. However, the SI-PT connection is typically absent in e-commerce product catalogs and expensive to construct manually due to...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	367,084
2008.05865	DF-GAN: A Simple and Effective Baseline for Text-to-Image Synthesis	Synthesizing high-quality realistic images from text descriptions is a challenging task. Existing text-to-image Generative Adversarial Networks generally employ a stacked architecture as the backbone yet still remain three flaws. First, the stacked architecture introduces the entanglements between generators of differe...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	191,639
2103.02370	FSDR: Frequency Space Domain Randomization for Domain Generalization	Domain generalization aims to learn a generalizable model from a known source domain for various unknown target domains. It has been studied widely by domain randomization that transfers source images to different styles in spatial space for learning domain-agnostic features. However, most existing randomization uses G...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	222,942
1906.02238	Adaptation Across Extreme Variations using Unlabeled Domain Bridges	We tackle an unsupervised domain adaptation problem for which the domain discrepancy between labeled source and unlabeled target domains is large, due to many factors of inter and intra-domain variation. While deep domain adaptation methods have been realized by reducing the domain discrepancy, these are difficult to a...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	133,974
2502.04121	Optimizing Perturbations for Improved Training of Machine Learning Models	Machine learning models have become indispensable tools in applications across the physical sciences. Their training is often time-consuming, vastly exceeding the inference timescales. Several protocols have been developed to perturb the learning process and improve the training, such as shrink and perturb, warm restar...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	530,991
2010.07175	New non-binary quantum codes from skew constacyclic codes over the ring $\mathbb{F}_{p^m}+v\mathbb{F}_{p^m}+v^2 \mathbb{F}_{p^m}$	In this article, we construct new non-binary quantum codes from skew constacyclic codes over finite commutative non-chain ring $\mathcal{R}= \mathbb{F}_{p^m}[v]/\langle v^3 =v \rangle$ where $p$ is an odd prime and $m \geq 1$. In order to obtain such quantum codes, first we study the structural properties of skew const...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	200,732
2211.10442	Deep learning methods for drug response prediction in cancer: predominant and emerging trends	Cancer claims millions of lives yearly worldwide. While many therapies have been made available in recent years, by in large cancer remains unsolved. Exploiting computational predictive models to study and treat cancer holds great promise in improving drug development and personalized design of treatment plans, ultimat...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	331,313
2411.04585	The State and Fate of Summarization Datasets: A Survey	Automatic summarization has consistently attracted attention due to its versatility and wide application in various downstream tasks. Despite its popularity, we find that annotation efforts have largely been disjointed, and have lacked common terminology. Consequently, it is challenging to discover existing resources o...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	506,329
2412.10915	C3: Learning Congestion Controllers with Formal Certificates	Learning-based congestion controllers offer better adaptability compared to traditional heuristic algorithms. However, the inherent unreliability of learning techniques can cause learning-based controllers to behave poorly, creating a need for formal guarantees. While methods for formally verifying learned congestion c...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	517,166
2309.12589	A Multi-Robot Task Assignment Framework for Search and Rescue with Heterogeneous Teams	In post-disaster scenarios, efficient search and rescue operations involve collaborative efforts between robots and humans. Existing planning approaches focus on specific aspects but overlook crucial elements like information gathering, task assignment, and planning. Furthermore, previous methods considering robot capa...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	393,851
1909.04246	Temporal Network Embedding with Micro- and Macro-dynamics	Network embedding aims to embed nodes into a low-dimensional space, while capturing the network structures and properties. Although quite a few promising network embedding methods have been proposed, most of them focus on static networks. In fact, temporal networks, which usually evolve over time in terms of microscopi...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	144,743
cmp-lg/9809003	A Comparison of WordNet and Roget's Taxonomy for Measuring Semantic Similarity	This paper presents the results of using Roget's International Thesaurus as the taxonomy in a semantic similarity measurement task. Four similarity metrics were taken from the literature and applied to Roget's The experimental evaluation suggests that the traditional edge counting approach does surprisingly well (a cor...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	536,927
2005.01157	Out of the Echo Chamber: Detecting Countering Debate Speeches	An educated and informed consumption of media content has become a challenge in modern times. With the shift from traditional news outlets to social media and similar venues, a major concern is that readers are becoming encapsulated in "echo chambers" and may fall prey to fake news and disinformation, lacking easy acce...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	175,504
1906.00180	Siamese recurrent networks learn first-order logic reasoning and exhibit zero-shot compositional generalization	Can neural nets learn logic? We approach this classic question with current methods, and demonstrate that recurrent neural networks can learn to recognize first order logical entailment relations between expressions. We define an artificial language in first-order predicate logic, generate a large dataset of sample 'se...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	133,293
1602.03534	Unsupervised Transductive Domain Adaptation	Supervised learning with large scale labeled datasets and deep layered models has made a paradigm shift in diverse areas in learning and recognition. However, this approach still suffers generalization issues under the presence of a domain shift between the training and the test data distribution. In this regard, unsup...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	52,010
2203.03597	Fast Rates for Noisy Interpolation Require Rethinking the Effects of Inductive Bias	Good generalization performance on high-dimensional data crucially hinges on a simple structure of the ground truth and a corresponding strong inductive bias of the estimator. Even though this intuition is valid for regularized models, in this paper we caution against a strong inductive bias for interpolation in the pr...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	284,148
2110.12263	Fixed-Time Convergent Distributed Observer Design of Linear Systems: A Kernel-Based Approach	The robust distributed state estimation for a class of continuous-time linear time-invariant systems is achieved by a novel kernel-based distributed observer, which, for the first time, ensures fixed-time convergence properties. The communication network between the agents is prescribed by a directed graph in which eac...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	262,775
cs/0604091	Robust Distributed Source Coding	We consider a distributed source coding system in which several observations are communicated to the decoder using limited transmission rate. The observations must be separately coded. We introduce a robust distributed coding scheme which flexibly trades off between system robustness and compression efficiency. The opt...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	539,406
1511.03546	Hierarchical Latent Semantic Mapping for Automated Topic Generation	Much of information sits in an unprecedented amount of text data. Managing allocation of these large scale text data is an important problem for many areas. Topic modeling performs well in this problem. The traditional generative models (PLSA,LDA) are the state-of-the-art approaches in topic modeling and most recent re...	false	false	false	false	false	true	true	false	true	false	false	false	false	false	false	false	false	false	48,767
1808.07913	Improving Abstraction in Text Summarization	Abstractive text summarization aims to shorten long text documents into a human readable form that contains the most important facts from the original document. However, the level of actual abstraction as measured by novel phrases that do not appear in the source document remains low in existing approaches. We propose ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	105,833
2305.18859	Large-scale Ridesharing DARP Instances Based on Real Travel Demand	Accurately predicting the real-life performance of algorithms solving the Dial-a-Ride Problem (DARP) in the context of Mobility on Demand (MoD) systems with ridesharing requires evaluating them on representative instances. However, the benchmarking of state-of-the-art DARP solution methods has been limited to small, ar...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	369,273
2207.04648	Learning Large-scale Universal User Representation with Sparse Mixture of Experts	Learning user sequence behaviour embedding is very sophisticated and challenging due to the complicated feature interactions over time and high dimensions of user features. Recent emerging foundation models, e.g., BERT and its variants, encourage a large body of researchers to investigate in this field. However, unlike...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	307,265
1401.6413	Predicting Nearly As Well As the Optimal Twice Differentiable Regressor	We study nonlinear regression of real valued data in an individual sequence manner, where we provide results that are guaranteed to hold without any statistical assumptions. We address the convergence and undertraining issues of conventional nonlinear regression methods and introduce an algorithm that elegantly mitigat...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	30,348
1901.11379	TUNet: Incorporating segmentation maps to improve classification	Determining the localization of specific protein in human cells is important for understanding cellular functions and biological processes of underlying diseases. Among imaging techniques, high-throughput fluorescence microscopy imaging is an efficient biotechnology to stain the protein of interest in a cell. In this w...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	120,235
2304.11514	Joint Beamforming and Phase Shift Design for Hybrid-IRS-and-UAV-aided Directional Modulation Network	Recently, intelligent reflecting surface (IRS) and unmanned aerial vehicle (UAV) have been introduced into wireless communication systems to enhance the performance of air-ground transmission. To make a good balance between performance, cost, and power consumption, a hybrid-IRS-and-UAV-assisted directional modulation (...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	359,845
2109.09559	Contrastive Learning of Subject-Invariant EEG Representations for Cross-Subject Emotion Recognition	EEG signals have been reported to be informative and reliable for emotion recognition in recent years. However, the inter-subject variability of emotion-related EEG signals still poses a great challenge for the practical applications of EEG-based emotion recognition. Inspired by recent neuroscience studies on inter-sub...	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	256,324
1907.12744	Not All Adversarial Examples Require a Complex Defense: Identifying Over-optimized Adversarial Examples with IQR-based Logit Thresholding	Detecting adversarial examples currently stands as one of the biggest challenges in the field of deep learning. Adversarial attacks, which produce adversarial examples, increase the prediction likelihood of a target class for a particular data point. During this process, the adversarial example can be further optimized...	false	false	false	false	false	false	true	false	false	false	false	true	true	false	false	false	false	false	140,193
2307.16331	Theoretically Principled Trade-off for Stateful Defenses against Query-Based Black-Box Attacks	Adversarial examples threaten the integrity of machine learning systems with alarming success rates even under constrained black-box conditions. Stateful defenses have emerged as an effective countermeasure, detecting potential attacks by maintaining a buffer of recent queries and detecting new queries that are too sim...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	382,563
2204.05189	MmWave 6D Radio Localization with a Snapshot Observation from a Single BS	Accurate and ubiquitous localization is crucial for a variety of applications such as logistics, navigation, intelligent transport, monitoring, control, and also for the benefit of communications. Exploiting millimeter-wave (mmWave) signals in 5G and Beyond 5G systems can provide accurate localization with limited infr...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	290,946
2411.14827	Physically Interpretable Probabilistic Domain Characterization	Characterizing domains is essential for models analyzing dynamic environments, as it allows them to adapt to evolving conditions or to hand the task over to backup systems when facing conditions outside their operational domain. Existing solutions typically characterize a domain by solving a regression or classificatio...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	510,339
2211.16068	ACE: Cooperative Multi-agent Q-learning with Bidirectional Action-Dependency	Multi-agent reinforcement learning (MARL) suffers from the non-stationarity problem, which is the ever-changing targets at every iteration when multiple agents update their policies at the same time. Starting from first principle, in this paper, we manage to solve the non-stationarity problem by proposing bidirectional...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	333,507
2401.08468	Nonparametric Evaluation of Noisy ICA Solutions	Independent Component Analysis (ICA) was introduced in the 1980's as a model for Blind Source Separation (BSS), which refers to the process of recovering the sources underlying a mixture of signals, with little knowledge about the source signals or the mixing process. While there are many sophisticated algorithms for e...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	421,898
cmp-lg/9606002	Clustered Language Models with Context-Equivalent States	In this paper, a hierarchical context definition is added to an existing clustering algorithm in order to increase its robustness. The resulting algorithm, which clusters contexts and events separately, is used to experiment with different ways of defining the context a language model takes into account. The contexts r...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	536,568
2006.03132	Earnings Prediction with Deep Learning	In the financial sector, a reliable forecast the future financial performance of a company is of great importance for investors' investment decisions. In this paper we compare long-term short-term memory (LSTM) networks to temporal convolution network (TCNs) in the prediction of future earnings per share (EPS). The exp...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	180,219
2412.15241	Quantifying Positional Biases in Text Embedding Models	Embedding models are crucial for tasks in Information Retrieval (IR) and semantic similarity measurement, yet their handling of longer texts and associated positional biases remains underexplored. In this study, we investigate the impact of content position and input size on text embeddings. Our experiments reveal that...	false	false	false	false	true	true	false	false	true	false	false	false	false	false	false	false	false	false	519,003
2109.15321	Sensor-Guided Optical Flow	This paper proposes a framework to guide an optical flow network with external cues to achieve superior accuracy either on known or unseen domains. Given the availability of sparse yet accurate optical flow hints from an external source, these are injected to modulate the correlation scores computed by a state-of-the-a...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	258,254
2305.17858	FastMESH: Fast Surface Reconstruction by Hexagonal Mesh-based Neural Rendering	Despite the promising results of multi-view reconstruction, the recent neural rendering-based methods, such as implicit surface rendering (IDR) and volume rendering (NeuS), not only incur a heavy computational burden on training but also have the difficulties in disentangling the geometric and appearance. Although havi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	368,773
2209.03236	Banknote Recognition for Visually Impaired People (Case of Ethiopian note)	Currency is used almost everywhere to facilitate business. In most developing countries, especially the ones in Africa, tangible notes are predominantly used in everyday financial transactions. One of these countries, Ethiopia, is believed to have one of the world highest rates of blindness (1.6%) and low vision (3.7%)...	true	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	316,448
2210.03484	Multi-objective and multi-fidelity Bayesian optimization of laser-plasma acceleration	Beam parameter optimization in accelerators involves multiple, sometimes competing objectives. Condensing these individual objectives into a single figure of merit unavoidably results in a bias towards particular outcomes, in absence of prior knowledge often in a non-desired way. Finding an optimal objective definition...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	322,056
2102.00675	Autonomous Navigation through intersections with Graph ConvolutionalNetworks and Conditional Imitation Learning for Self-driving Cars	In autonomous driving, navigation through unsignaled intersections with many traffic participants moving around is a challenging task. To provide a solution to this problem, we propose a novel branched network G-CIL for the navigation policy learning. Specifically, we firstly represent such dynamic environments as grap...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	217,875
2202.09508	Who Are the Best Adopters? User Selection Model for Free Trial Item Promotion	With the increasingly fierce market competition, offering a free trial has become a potent stimuli strategy to promote products and attract users. By providing users with opportunities to experience goods without charge, a free trial makes adopters know more about products and thus encourages their willingness to buy. ...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	281,223
1810.01367	FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models	A promising class of generative models maps points from a simple distribution to a complex distribution through an invertible neural network. Likelihood-based training of these models requires restricting their architectures to allow cheap computation of Jacobian determinants. Alternatively, the Jacobian trace can be u...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	109,379
1811.00681	On the Generation of Medical Question-Answer Pairs	Question answering (QA) has achieved promising progress recently. However, answering a question in real-world scenarios like the medical domain is still challenging, due to the requirement of external knowledge and the insufficient quantity of high-quality training data. In the light of these challenges, we study the t...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	112,159
2208.01750	Optimizing Information Freshness Leveraging Multi-RISs in NOMA-based IoT Networks	This paper investigates the benefits of integrating multiple reconfigurable intelligent surfaces (RISs) in enhancing the timeliness performance of uplink Internet-of-Things (IoT) network, where IoT devices (IoTDs) upload their time-stamped status update information to a base station (BS) using non-orthogonal multiple a...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	311,252
2306.01704	Temporal-controlled Frame Swap for Generating High-Fidelity Stereo Driving Data for Autonomy Analysis	This paper presents a novel approach, TeFS (Temporal-controlled Frame Swap), to generate synthetic stereo driving data for visual simultaneous localization and mapping (vSLAM) tasks. TeFS is designed to overcome the lack of native stereo vision support in commercial driving simulators, and we demonstrate its effectiven...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	370,552
1912.01673	COSTRA 1.0: A Dataset of Complex Sentence Transformations	We present COSTRA 1.0, a dataset of complex sentence transformations. The dataset is intended for the study of sentence-level embeddings beyond simple word alternations or standard paraphrasing. This first version of the dataset is limited to sentences in Czech but the construction method is universal and we plan to us...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	156,141
1803.03772	Generalization and Expressivity for Deep Nets	Along with the rapid development of deep learning in practice, the theoretical explanations for its success become urgent. Generalization and expressivity are two widely used measurements to quantify theoretical behaviors of deep learning. The expressivity focuses on finding functions expressible by deep nets but canno...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	92,319
2406.10534	Finite-difference-informed graph network for solving steady-state incompressible flows on block-structured grids	Advances in deep learning have enabled physics-informed neural networks to solve partial differential equations. Numerical differentiation using the finite-difference (FD) method is efficient in physics-constrained designs, even in parameterized settings. In traditional computational fluid dynamics(CFD), body-fitted bl...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	464,447
1705.08966	Communication vs Distributed Computation: an alternative trade-off curve	In this paper, we revisit the communication vs. distributed computing trade-off, studied within the framework of MapReduce in [1]. An implicit assumption in the aforementioned work is that each server performs all possible computations on all the files stored in its memory. Our starting observation is that, if servers ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	74,117
2303.12002	End-to-End Integration of Speech Separation and Voice Activity Detection for Low-Latency Diarization of Telephone Conversations	Recent works show that speech separation guided diarization (SSGD) is an increasingly promising direction, mainly thanks to the recent progress in speech separation. It performs diarization by first separating the speakers and then applying voice activity detection (VAD) on each separated stream. In this work we conduc...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	353,099
2403.02405	Classification of the Fashion-MNIST Dataset on a Quantum Computer	The potential impact of quantum machine learning algorithms on industrial applications remains an exciting open question. Conventional methods for encoding classical data into quantum computers are not only too costly for a potential quantum advantage in the algorithms but also severely limit the scale of feasible expe...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	434,790
2404.10760	Learning Feature Inversion for Multi-class Anomaly Detection under General-purpose COCO-AD Benchmark	Anomaly detection (AD) is often focused on detecting anomaly areas for industrial quality inspection and medical lesion examination. However, due to the specific scenario targets, the data scale for AD is relatively small, and evaluation metrics are still deficient compared to classic vision tasks, such as object detec...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	447,238
2105.14216	CDMA: A Practical Cross-Device Federated Learning Algorithm for General Minimax Problems	Minimax problems arise in a wide range of important applications including robust adversarial learning and Generative Adversarial Network (GAN) training. Recently, algorithms for minimax problems in the Federated Learning (FL) paradigm have received considerable interest. Existing federated algorithms for general minim...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	237,585
1906.11889	Deep Eyedentification: Biometric Identification using Micro-Movements of the Eye	We study involuntary micro-movements of the eye for biometric identification. While prior studies extract lower-frequency macro-movements from the output of video-based eye-tracking systems and engineer explicit features of these macro-movements, we develop a deep convolutional architecture that processes the raw eye-t...	true	false	false	false	false	false	true	false	true	false	false	true	false	false	false	false	false	false	136,779
2108.13831	Deep Learning of Transferable MIMO Channel Modes for 6G V2X Communications	In the emerging high mobility Vehicle-to-Everything (V2X) communications using millimeter Wave (mmWave) and sub-THz, Multiple-Input Multiple-Output (MIMO) channel estimation is an extremely challenging task. At mmWaves/sub-THz frequencies, MIMO channels exhibit few leading paths in the space-time domain (i.e., directio...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	252,919
2304.08912	Generalized Weak Supervision for Neural Information Retrieval	Neural ranking models (NRMs) have demonstrated effective performance in several information retrieval (IR) tasks. However, training NRMs often requires large-scale training data, which is difficult and expensive to obtain. To address this issue, one can train NRMs via weak supervision, where a large dataset is automati...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	358,867
2502.11331	Transfer Learning of CATE with Kernel Ridge Regression	The proliferation of data has sparked significant interest in leveraging findings from one study to estimate treatment effects in a different target population without direct outcome observations. However, the transfer learning process is frequently hindered by substantial covariate shift and limited overlap between (i...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	534,305
1912.06602	That and There: Judging the Intent of Pointing Actions with Robotic Arms	Collaborative robotics requires effective communication between a robot and a human partner. This work proposes a set of interpretive principles for how a robotic arm can use pointing actions to communicate task information to people by extending existing models from the related literature. These principles are evaluat...	true	false	false	false	true	false	false	true	false	false	false	true	false	false	false	false	false	false	157,379
2106.08527	FAIR: Fairness-Aware Information Retrieval Evaluation	With the emerging needs of creating fairness-aware solutions for search and recommendation systems, a daunting challenge exists of evaluating such solutions. While many of the traditional information retrieval (IR) metrics can capture the relevance, diversity, and novelty for the utility with respect to users, they are...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	241,323
2312.10771	kNN-ICL: Compositional Task-Oriented Parsing Generalization with Nearest Neighbor In-Context Learning	Task-Oriented Parsing (TOP) enables conversational assistants to interpret user commands expressed in natural language, transforming them into structured outputs that combine elements of both natural language and intent/slot tags. Recently, Large Language Models (LLMs) have achieved impressive performance in synthesizi...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	416,312
2408.08671	Towards Physical World Backdoor Attacks against Skeleton Action Recognition	Skeleton Action Recognition (SAR) has attracted significant interest for its efficient representation of the human skeletal structure. Despite its advancements, recent studies have raised security concerns in SAR models, particularly their vulnerability to adversarial attacks. However, such strategies are limited to di...	false	false	false	false	false	false	false	false	false	false	false	true	true	false	false	false	false	false	481,098
2004.13877	Classifying Image Sequences of Astronomical Transients with Deep Neural Networks	Supervised classification of temporal sequences of astronomical images into meaningful transient astrophysical phenomena has been considered a hard problem because it requires the intervention of human experts. The classifier uses the expert's knowledge to find heuristic features to process the images, for instance, by...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	174,701
2403.09762	Emotional Intelligence Through Artificial Intelligence : NLP and Deep Learning in the Analysis of Healthcare Texts	This manuscript presents a methodical examination of the utilization of Artificial Intelligence in the assessment of emotions in texts related to healthcare, with a particular focus on the incorporation of Natural Language Processing and deep learning technologies. We scrutinize numerous research studies that employ AI...	true	false	false	false	true	false	true	false	true	false	false	false	false	false	false	true	false	false	437,911
2305.19187	Generating with Confidence: Uncertainty Quantification for Black-box Large Language Models	Large language models (LLMs) specializing in natural language generation (NLG) have recently started exhibiting promising capabilities across a variety of domains. However, gauging the trustworthiness of responses generated by LLMs remains an open challenge, with limited research on uncertainty quantification (UQ) for ...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	369,413
2501.14189	Distributed Multi-Agent Coordination Using Multi-Modal Foundation Models	Distributed Constraint Optimization Problems (DCOPs) offer a powerful framework for multi-agent coordination but often rely on labor-intensive, manual problem construction. To address this, we introduce VL-DCOPs, a framework that takes advantage of large multimodal foundation models (LFMs) to automatically generate con...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	true	false	false	false	527,015
1805.10727	Perceive Your Users in Depth: Learning Universal User Representations from Multiple E-commerce Tasks	Tasks such as search and recommendation have become increas- ingly important for E-commerce to deal with the information over- load problem. To meet the diverse needs of di erent users, person- alization plays an important role. In many large portals such as Taobao and Amazon, there are a bunch of di erent types of sea...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	98,755
2306.02577	Exploring the Role of the Bottleneck in Slot-Based Models Through Covariance Regularization	In this project we attempt to make slot-based models with an image reconstruction objective competitive with those that use a feature reconstruction objective on real world datasets. We propose a loss-based approach to constricting the bottleneck of slot-based models, allowing larger-capacity encoder networks to be use...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	370,960
2004.07822	Order Matters: Generating Progressive Explanations for Planning Tasks in Human-Robot Teaming	Prior work on generating explanations in a planning and decision-making context has focused on providing the rationale behind an AI agent's decision making. While these methods provide the right explanations from the explainer's perspective, they fail to heed the cognitive requirement of understanding an explanation fr...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	172,887
1707.01826	Indefinite Kernel Logistic Regression with Concave-inexact-convex Procedure	In kernel methods, the kernels are often required to be positive definite, which restricts the use of many indefinite kernels. To consider those non-positive definite kernels, in this paper, we aim to build an indefinite kernel learning framework for kernel logistic regression. The proposed indefinite kernel logistic r...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	76,602
2302.03306	Mismatched estimation of non-symmetric rank-one matrices corrupted by structured noise	We study the performance of a Bayesian statistician who estimates a rank-one signal corrupted by non-symmetric rotationally invariant noise with a generic distribution of singular values. As the signal-to-noise ratio and the noise structure are unknown, a Gaussian setup is incorrectly assumed. We derive the exact analy...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	344,297
1806.06411	Measuring Semantic Coherence of a Conversation	Conversational systems have become increasingly popular as a way for humans to interact with computers. To be able to provide intelligent responses, conversational systems must correctly model the structure and semantics of a conversation. We introduce the task of measuring semantic (in)coherence in a conversation with...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	100,697
2112.09039	Hypercontractive inequalities for the second norm of highly concentrated functions, and Mrs. Gerber's-type inequalities for the second Renyi entropy	Let $T_{\epsilon}$, $0 \le \epsilon \le 1/2$, be the noise operator acting on functions on the boolean cube $\{0,1\}^n$. Let $f$ be a distribution on $\{0,1\}^n$ and let $q > 1$. We prove tight Mrs. Gerber-type results for the second Renyi entropy of $T_{\epsilon} f$ which take into account the value of the $q^{th}$ Re...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	272,010
2301.00497	Efficient Online Learning with Memory via Frank-Wolfe Optimization: Algorithms with Bounded Dynamic Regret and Applications to Control	Projection operations are a typical computation bottleneck in online learning. In this paper, we enable projection-free online learning within the framework of Online Convex Optimization with Memory (OCO-M) -- OCO-M captures how the history of decisions affects the current outcome by allowing the online learning loss f...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	338,927
2006.07554	Online Hyper-parameter Tuning in Off-policy Learning via Evolutionary Strategies	Off-policy learning algorithms have been known to be sensitive to the choice of hyper-parameters. However, unlike near on-policy algorithms for which hyper-parameters could be optimized via e.g. meta-gradients, similar techniques could not be straightforwardly applied to off-policy learning. In this work, we propose a ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	181,848
2112.02812	User behavior understanding in real world settings	How to extract meaningful information in user historical behavior plays a crucial role in recommendation. User behavior sequence often contains multiple conceptually distinct items that belong to different item groups and the number of the item groups is changing over time. It is necessary to learn a dynamic group of r...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	269,972
2007.11571	Neural Sparse Voxel Fields	Photo-realistic free-viewpoint rendering of real-world scenes using classical computer graphics techniques is challenging, because it requires the difficult step of capturing detailed appearance and geometry models. Recent studies have demonstrated promising results by learning scene representations that implicitly enc...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	true	188,579
2310.09338	Uncertainty Quantification using Generative Approach	We present the Incremental Generative Monte Carlo (IGMC) method, designed to measure uncertainty in deep neural networks using deep generative approaches. IGMC iteratively trains generative models, adding their output to the dataset, to compute the posterior distribution of the expectation of a random variable. We prov...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	399,735
2210.04655	A CNN Based Approach for the Point-Light Photometric Stereo Problem	Reconstructing the 3D shape of an object using several images under different light sources is a very challenging task, especially when realistic assumptions such as light propagation and attenuation, perspective viewing geometry and specular light reflection are considered. Many of works tackling Photometric Stereo (P...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	322,542
1601.06892	ReconNet: Non-Iterative Reconstruction of Images from Compressively Sensed Random Measurements	The goal of this paper is to present a non-iterative and more importantly an extremely fast algorithm to reconstruct images from compressively sensed (CS) random measurements. To this end, we propose a novel convolutional neural network (CNN) architecture which takes in CS measurements of an image as input and outputs ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	51,355
1911.09287	Band-limited Training and Inference for Convolutional Neural Networks	The convolutional layers are core building blocks of neural network architectures. In general, a convolutional filter applies to the entire frequency spectrum of the input data. We explore artificially constraining the frequency spectra of these filters and data, called band-limiting, during training. The frequency dom...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	154,464
2201.11697	Constrained Structure Learning for Scene Graph Generation	As a structured prediction task, scene graph generation aims to build a visually-grounded scene graph to explicitly model objects and their relationships in an input image. Currently, the mean field variational Bayesian framework is the de facto methodology used by the existing methods, in which the unconstrained infer...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	277,376
1704.05016	CNN Feature boosted SeqSLAM for Real-Time Loop Closure Detection	Loop closure detection (LCD) is an indispensable part of simultaneous localization and mapping systems (SLAM); it enables robots to produce a consistent map by recognizing previously visited places. When robots operate over extended periods, robustness to viewpoint and condition changes as well as satisfactory real-tim...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	71,931
1708.08421	Directional Compactly supported Box Spline Tight Framelets with Simple Structure	To effectively capture singularities in high-dimensional data and functions, multivariate compactly supported tight framelets, having directionality and derived from refinable box splines, are of particular interest in both theory and applications. The $d$-dimensional Haar refinable function $\chi_{[0,1]^d}$ is a simpl...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	79,642
1805.07024	Gated Recurrent Unit Based Acoustic Modeling with Future Context	The use of future contextual information is typically shown to be helpful for acoustic modeling. However, for the recurrent neural network (RNN), it's not so easy to model the future temporal context effectively, meanwhile keep lower model latency. In this paper, we attempt to design a RNN acoustic model that being cap...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	97,721

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