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
1509.01822	The MIMO Wiretap Channel Decomposed	The problem of sending a secret message over the Gaussian multiple-input multiple-output (MIMO) wiretap channel is studied. While the capacity of this channel is known, it is not clear how to construct optimal coding schemes that achieve this capacity. In this work, we use linear operations along with successive interf...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	46,659
1905.07710	A 2D dilated residual U-Net for multi-organ segmentation in thoracic CT	Automatic segmentation of organs-at-risk (OAR) in computed tomography (CT) is an essential part of planning effective treatment strategies to combat lung and esophageal cancer. Accurate segmentation of organs surrounding tumours helps account for the variation in position and morphology inherent across patients, thereb...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	131,310
1308.0702	Universal Empathy and Ethical Bias for Artificial General Intelligence	Rational agents are usually built to maximize rewards. However, AGI agents can find undesirable ways of maximizing any prior reward function. Therefore value learning is crucial for safe AGI. We assume that generalized states of the world are valuable - not rewards themselves, and propose an extension of AIXI, in which...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	26,245
1608.04185	Learning to Rank Questions for Community Question Answering with Ranking SVM	This paper presents our method to retrieve relevant queries given a new question in the context of Discovery Challenge: Learning to Re-Ranking Questions for Community Question Answering competition. In order to do that, a set of learning to rank methods was investigated to select an appropriate method. The selected met...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	59,790
2111.12849	Particle Graph Autoencoders and Differentiable, Learned Energy Mover's Distance	Autoencoders have useful applications in high energy physics in anomaly detection, particularly for jets - collimated showers of particles produced in collisions such as those at the CERN Large Hadron Collider. We explore the use of graph-based autoencoders, which operate on jets in their "particle cloud" representatio...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	268,092
2011.05867	DeepI2I: Enabling Deep Hierarchical Image-to-Image Translation by Transferring from GANs	Image-to-image translation has recently achieved remarkable results. But despite current success, it suffers from inferior performance when translations between classes require large shape changes. We attribute this to the high-resolution bottlenecks which are used by current state-of-the-art image-to-image methods. Th...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	206,068
2405.08263	Palette-based Color Transfer between Images	As an important subtopic of image enhancement, color transfer aims to enhance the color scheme of a source image according to a reference one while preserving the semantic context. To implement color transfer, the palette-based color mapping framework was proposed. \textcolor{black}{It is a classical solution that does...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	454,036
2406.19081	Unsupervised Latent Stain Adaptation for Computational Pathology	In computational pathology, deep learning (DL) models for tasks such as segmentation or tissue classification are known to suffer from domain shifts due to different staining techniques. Stain adaptation aims to reduce the generalization error between different stains by training a model on source stains that generaliz...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	468,295
1809.03171	The AAU Multimodal Annotation Toolboxes: Annotating Objects in Images and Videos	This tech report gives an introduction to two annotation toolboxes that enable the creation of pixel and polygon-based masks as well as bounding boxes around objects of interest. Both toolboxes support the annotation of sequential images in the RGB and thermal modalities. Each annotated object is assigned a classificat...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	107,255
1607.06667	Inpainting of long audio segments with similarity graphs	We present a novel method for the compensation of long duration data loss in audio signals, in particular music. The concealment of such signal defects is based on a graph that encodes signal structure in terms of time-persistent spectral similarity. A suitable candidate segment for the substitution of the lost content...	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	58,914
1810.09720	Color naming guided intrinsic image decomposition	Intrinsic image decomposition is a severely under-constrained problem. User interactions can help to reduce the ambiguity of the decomposition considerably. The traditional way of user interaction is to draw scribbles that indicate regions with constant reflectance or shading. However the effect scopes of the scribbles...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	111,115
1604.07088	Effect of User Mobility on the Performance of Device-to-Device Networks with Distributed Caching	We consider a distributed caching device-to-device (D2D) network in which a user's file of interest is cached as several portions in the storage of other devices in the network. Assuming that the user needs to obtain all these file portions, the portions cached farther away naturally become the performance bottleneck. ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	55,037
1804.07853	What's Going On in Neural Constituency Parsers? An Analysis	A number of differences have emerged between modern and classic approaches to constituency parsing in recent years, with structural components like grammars and feature-rich lexicons becoming less central while recurrent neural network representations rise in popularity. The goal of this work is to analyze the extent t...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	95,619
2405.07943	Decision Mamba Architectures	Recent advancements in imitation learning have been largely fueled by the integration of sequence models, which provide a structured flow of information to effectively mimic task behaviours. Currently, Decision Transformer (DT) and subsequently, the Hierarchical Decision Transformer (HDT), presented Transformer-based a...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	453,925
2001.10839	A kind of quaternary sequences of period $2p^mq^n$ and their linear complexity	Sequences with high linear complexity have wide applications in cryptography. In this paper, a new class of quaternary sequences over $\mathbb{F}_4$ with period $2p^mq^n$ is constructed using generalized cyclotomic classes. Results show that the linear complexity of these sequences attains the maximum.	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	161,919
1905.01395	On the Difficulty of Evaluating Baselines: A Study on Recommender Systems	Numerical evaluations with comparisons to baselines play a central role when judging research in recommender systems. In this paper, we show that running baselines properly is difficult. We demonstrate this issue on two extensively studied datasets. First, we show that results for baselines that have been used in numer...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	129,716
2211.05764	DC-Check: A Data-Centric AI checklist to guide the development of reliable machine learning systems	While there have been a number of remarkable breakthroughs in machine learning (ML), much of the focus has been placed on model development. However, to truly realize the potential of machine learning in real-world settings, additional aspects must be considered across the ML pipeline. Data-centric AI is emerging as a ...	false	false	false	false	true	false	true	false	false	false	false	false	false	true	false	false	false	true	329,669
2012.02593	On Attitude Recovery of Spacecraft using Nonlinear Control	The general objective of this Ph.D. thesis is to study the dynamics and control of rigid and flexible spacecraft supported by a high-fidelity numerical simulation environment. The demand for greater attitude pointing precision, attitude maneuvering or recovery with the increased use of lightweight and flexible material...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	209,820
1907.07433	Towards Blockchain-based Multi-Agent Robotic Systems: Analysis, Classification and Applications	Decentralization, immutability and transparency make of Blockchain one of the most innovative technology of recent years. This paper presents an overview of solutions based on Blockchain technology for multi-agent robotic systems, and provide an analysis and classification of this emerging field. The reasons for implem...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	true	138,876
2106.09898	Bad Characters: Imperceptible NLP Attacks	Several years of research have shown that machine-learning systems are vulnerable to adversarial examples, both in theory and in practice. Until now, such attacks have primarily targeted visual models, exploiting the gap between human and machine perception. Although text-based models have also been attacked with adver...	false	false	false	false	false	false	true	false	true	false	false	false	true	false	false	false	false	false	241,830
1712.00866	Raw Waveform-based Audio Classification Using Sample-level CNN Architectures	Music, speech, and acoustic scene sound are often handled separately in the audio domain because of their different signal characteristics. However, as the image domain grows rapidly by versatile image classification models, it is necessary to study extensible classification models in the audio domain as well. In this ...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	85,997
2409.13268	JoyHallo: Digital human model for Mandarin	In audio-driven video generation, creating Mandarin videos presents significant challenges. Collecting comprehensive Mandarin datasets is difficult, and the complex lip movements in Mandarin further complicate model training compared to English. In this study, we collected 29 hours of Mandarin speech video from JD Heal...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	489,922
2410.01774	Trained Transformer Classifiers Generalize and Exhibit Benign Overfitting In-Context	Transformers have the capacity to act as supervised learning algorithms: by properly encoding a set of labeled training ("in-context") examples and an unlabeled test example into an input sequence of vectors of the same dimension, the forward pass of the transformer can produce predictions for that unlabeled test examp...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	493,938
2202.11486	Augmentation based unsupervised domain adaptation	The insertion of deep learning in medical image analysis had lead to the development of state-of-the art strategies in several applications such a disease classification, as well as abnormality detection and segmentation. However, even the most advanced methods require a huge and diverse amount of data to generalize. B...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	281,907
1905.01639	Deep Video Inpainting	Video inpainting aims to fill spatio-temporal holes with plausible content in a video. Despite tremendous progress of deep neural networks for image inpainting, it is challenging to extend these methods to the video domain due to the additional time dimension. In this work, we propose a novel deep network architecture ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	129,774
1907.09014	Learning Hybrid Object Kinematics for Efficient Hierarchical Planning Under Uncertainty	Sudden changes in the dynamics of robotic tasks, such as contact with an object or the latching of a door, are often viewed as inconvenient discontinuities that make manipulation difficult. However, when these transitions are well-understood, they can be leveraged to reduce uncertainty or aid manipulation---for example...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	139,244
1707.03872	Independence, Conditionality and Structure of Dempster-Shafer Belief Functions	Several approaches of structuring (factorization, decomposition) of Dempster-Shafer joint belief functions from literature are reviewed with special emphasis on their capability to capture independence from the point of view of the claim that belief functions generalize bayes notion of probability. It is demonstrated...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	76,945
2306.17595	RBSR: Efficient and Flexible Recurrent Network for Burst Super-Resolution	Burst super-resolution (BurstSR) aims at reconstructing a high-resolution (HR) image from a sequence of low-resolution (LR) and noisy images, which is conducive to enhancing the imaging effects of smartphones with limited sensors. The main challenge of BurstSR is to effectively combine the complementary information fro...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	376,756
2210.11238	Analysis of Smooth Pursuit Assessment in Virtual Reality and Concussion Detection using BiLSTM	The sport-related concussion (SRC) battery relies heavily upon subjective symptom reporting in order to determine the diagnosis of a concussion. Unfortunately, athletes with SRC may return-to-play (RTP) too soon if they are untruthful of their symptoms. It is critical to provide accurate assessments that can overcome u...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	325,238
2209.03715	Optimization-based framework for low-voltage grid reinforcement assessment under various levels of flexibility and coordination	The rapid electrification of residential heating and mobility sectors is expected to drive the existing distribution grid assets beyond their planned operating conditions. This change will also reveal new potentials through sector coupling, flexibilities, and the local exchange of decentralized generation. This paper t...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	316,575
2104.04295	Transforming Feature Space to Interpret Machine Learning Models	Model-agnostic tools for interpreting machine-learning models struggle to summarize the joint effects of strongly dependent features in high-dimensional feature spaces, which play an important role in pattern recognition, for example in remote sensing of landcover. This contribution proposes a novel approach that inter...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	229,351
2304.11267	Speed Is All You Need: On-Device Acceleration of Large Diffusion Models via GPU-Aware Optimizations	The rapid development and application of foundation models have revolutionized the field of artificial intelligence. Large diffusion models have gained significant attention for their ability to generate photorealistic images and support various tasks. On-device deployment of these models provides benefits such as lowe...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	359,743
1911.04580	Supervised Initialization of LSTM Networks for Fundamental Frequency Detection in Noisy Speech Signals	Fundamental frequency is one of the most important parameters of human speech, of importance for the classification of accent, gender, speaking styles, speaker identification, age, among others. The proper detection of this parameter remains as an important challenge for severely degraded signals. In previous reference...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	153,023
2208.08100	CommitBART: A Large Pre-trained Model for GitHub Commits	GitHub commits, which record the code changes with natural language messages for description, play a critical role for software developers to comprehend the software evolution. To promote the development of the open-source software community, we collect a commit benchmark including over 7.99 million commits across 7 pr...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	313,260
2104.01231	Diverse Gaussian Noise Consistency Regularization for Robustness and Uncertainty Calibration	Deep neural networks achieve high prediction accuracy when the train and test distributions coincide. In practice though, various types of corruptions occur which deviate from this setup and cause severe performance degradations. Few methods have been proposed to address generalization in the presence of unforeseen dom...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	228,273
2310.05644	Diagnosing Catastrophe: Large parts of accuracy loss in continual learning can be accounted for by readout misalignment	Unlike primates, training artificial neural networks on changing data distributions leads to a rapid decrease in performance on old tasks. This phenomenon is commonly referred to as catastrophic forgetting. In this paper, we investigate the representational changes that underlie this performance decrease and identify t...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	398,231
1911.08196	Defending with Shared Resources on a Network	In this paper we consider a defending problem on a network. In the model, the defender holds a total defending resource of R, which can be distributed to the nodes of the network. The defending resource allocated to a node can be shared by its neighbors. There is a weight associated with every edge that represents the ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	154,125
2302.13711	Internal-Coordinate Density Modelling of Protein Structure: Covariance Matters	After the recent ground-breaking advances in protein structure prediction, one of the remaining challenges in protein machine learning is to reliably predict distributions of structural states. Parametric models of fluctuations are difficult to fit due to complex covariance structures between degrees of freedom in the ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	348,039
2411.01348	Optimizing Violence Detection in Video Classification Accuracy through 3D Convolutional Neural Networks	As violent crimes continue to happen, it becomes necessary to have security cameras that can rapidly identify moments of violence with excellent accuracy. The purpose of this study is to identify how many frames should be analyzed at a time in order to optimize a violence detection model's accuracy as a parameter of th...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	505,027
2303.02423	Estimating Age of Information Using Finite Order Moments	Age of information (AoI) has been proposed as a more suitable metric for characterizing the freshness of information than traditional metrics like delay and throughput. However, the calculation of AoI requires complex analysis and strict end-to-end synchronization. Most existential AoI-related works have assumed that t...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	349,352
2309.17182	RECOMBINER: Robust and Enhanced Compression with Bayesian Implicit Neural Representations	COMpression with Bayesian Implicit NEural Representations (COMBINER) is a recent data compression method that addresses a key inefficiency of previous Implicit Neural Representation (INR)-based approaches: it avoids quantization and enables direct optimization of the rate-distortion performance. However, COMBINER still...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	395,668
1907.05855	DisCoRL: Continual Reinforcement Learning via Policy Distillation	In multi-task reinforcement learning there are two main challenges: at training time, the ability to learn different policies with a single model; at test time, inferring which of those policies applying without an external signal. In the case of continual reinforcement learning a third challenge arises: learning tasks...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	138,470
2411.15605	GIFT: A Framework for Global Interpretable Faithful Textual Explanations of Vision Classifiers	Understanding deep models is crucial for deploying them in safety-critical applications. We introduce GIFT, a framework for deriving post-hoc, global, interpretable, and faithful textual explanations for vision classifiers. GIFT starts from local faithful visual counterfactual explanations and employs (vision) language...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	510,684
2412.03527	FANAL -- Financial Activity News Alerting Language Modeling Framework	In the rapidly evolving financial sector, the accurate and timely interpretation of market news is essential for stakeholders needing to navigate unpredictable events. This paper introduces FANAL (Financial Activity News Alerting Language Modeling Framework), a specialized BERT-based framework engineered for real-time ...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	513,993
2111.04525	D-Flow: A Real Time Spatial Temporal Model for Target Area Segmentation	Semantic segmentation has attracted a large amount of attention in recent years. In robotics, segmentation can be used to identify a region of interest, or \emph{target area}. For example, in the RoboCup Standard Platform League (SPL), segmentation separates the soccer field from the background and from players on the ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	265,508
2003.04774	ENTMOOT: A Framework for Optimization over Ensemble Tree Models	Gradient boosted trees and other regression tree models perform well in a wide range of real-world, industrial applications. These tree models (i) offer insight into important prediction features, (ii) effectively manage sparse data, and (iii) have excellent prediction capabilities. Despite their advantages, they are g...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	167,659
2303.01258	Domain-adapted large language models for classifying nuclear medicine reports	With the growing use of transformer-based language models in medicine, it is unclear how well these models generalize to nuclear medicine which has domain-specific vocabulary and unique reporting styles. In this study, we evaluated the value of domain adaptation in nuclear medicine by adapting language models for the p...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	348,899
2102.09397	Meta-Transfer Learning for Low-Resource Abstractive Summarization	Neural abstractive summarization has been studied in many pieces of literature and achieves great success with the aid of large corpora. However, when encountering novel tasks, one may not always benefit from transfer learning due to the domain shifting problem, and overfitting could happen without adequate labeled exa...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	220,767
2012.08645	An anatomically-informed 3D CNN for brain aneurysm classification with weak labels	A commonly adopted approach to carry out detection tasks in medical imaging is to rely on an initial segmentation. However, this approach strongly depends on voxel-wise annotations which are repetitive and time-consuming to draw for medical experts. An interesting alternative to voxel-wise masks are so-called "weak" la...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	211,818
1508.03891	REBA: A Refinement-Based Architecture for Knowledge Representation and Reasoning in Robotics	This paper describes an architecture for robots that combines the complementary strengths of probabilistic graphical models and declarative programming to represent and reason with logic-based and probabilistic descriptions of uncertainty and domain knowledge. An action language is extended to support non-boolean fluen...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	true	46,057
2002.07756	Hierarchical Correlation Clustering and Tree Preserving Embedding	We propose a hierarchical correlation clustering method that extends the well-known correlation clustering to produce hierarchical clusters applicable to both positive and negative pairwise dissimilarities. Then, in the following, we study unsupervised representation learning with such hierarchical correlation clusteri...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	164,562
2409.15680	Distributed Online Bandit Nonconvex Optimization with One-Point Residual Feedback via Dynamic Regret	This paper considers the distributed online bandit optimization problem with nonconvex loss functions over a time-varying digraph. This problem can be viewed as a repeated game between a group of online players and an adversary. At each round, each player selects a decision from the constraint set, and then the adversa...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	491,008
1711.08141	Shift: A Zero FLOP, Zero Parameter Alternative to Spatial Convolutions	Neural networks rely on convolutions to aggregate spatial information. However, spatial convolutions are expensive in terms of model size and computation, both of which grow quadratically with respect to kernel size. In this paper, we present a parameter-free, FLOP-free "shift" operation as an alternative to spatial co...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	85,141
2411.16498	Multi-Resolution Generative Modeling of Human Motion from Limited Data	We present a generative model that learns to synthesize human motion from limited training sequences. Our framework provides conditional generation and blending across multiple temporal resolutions. The model adeptly captures human motion patterns by integrating skeletal convolution layers and a multi-scale architectur...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	true	511,048
2307.09365	An Evaluation of Zero-Cost Proxies -- from Neural Architecture Performance to Model Robustness	Zero-cost proxies are nowadays frequently studied and used to search for neural architectures. They show an impressive ability to predict the performance of architectures by making use of their untrained weights. These techniques allow for immense search speed-ups. So far the joint search for well-performing and robust...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	380,149
2008.02714	Multi-source Heterogeneous Domain Adaptation with Conditional Weighting Adversarial Network	Heterogeneous domain adaptation (HDA) tackles the learning of cross-domain samples with both different probability distributions and feature representations. Most of the existing HDA studies focus on the single-source scenario. In reality, however, it is not uncommon to obtain samples from multiple heterogeneous domain...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	190,690
2206.03334	Correlations of network trajectories	Temporal networks model how the interaction between elements in a complex system evolve over time. Just like complex systems display collective dynamics, here we interpret temporal networks as trajectories performing a collective motion in graph space, following a latent graph dynamical system. Under this paradigm, we ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	301,239
1808.10524	Total Recall: Understanding Traffic Signs using Deep Hierarchical Convolutional Neural Networks	Recognizing Traffic Signs using intelligent systems can drastically reduce the number of accidents happening world-wide. With the arrival of Self-driving cars it has become a staple challenge to solve the automatic recognition of Traffic and Hand-held signs in the major streets. Various machine learning techniques like...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	106,393
2111.03536	A Unified Game-Theoretic Interpretation of Adversarial Robustness	This paper provides a unified view to explain different adversarial attacks and defense methods, \emph{i.e.} the view of multi-order interactions between input variables of DNNs. Based on the multi-order interaction, we discover that adversarial attacks mainly affect high-order interactions to fool the DNN. Furthermore...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	265,205
2111.13331	Impact of classification difficulty on the weight matrices spectra in Deep Learning and application to early-stopping	Much research effort has been devoted to explaining the success of deep learning. Random Matrix Theory (RMT) provides an emerging way to this end: spectral analysis of large random matrices involved in a trained deep neural network (DNN) such as weight matrices or Hessian matrices with respect to the stochastic gradien...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	268,267
2209.04701	Subdiffusive semantic evolution in Indo-European languages	How do words change their meaning? Although semantic evolution is driven by a variety of distinct factors, including linguistic, societal, and technological ones, we find that there is one law that holds universally across five major Indo-European languages: that semantic evolution is strongly subdiffusive. Using an au...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	316,859
2101.12370	An Automated Theorem Proving Framework for Information-Theoretic Results	We present a versatile automated theorem proving framework capable of automated discovery, simplification and proofs of inner and outer bounds in network information theory, deduction of properties of information-theoretic quantities (e.g. Wyner and G\'acs-K\"orner common information), and discovery of non-Shannon-type...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	217,544
2112.08351	Database Search Results Disambiguation for Task-Oriented Dialog Systems	As task-oriented dialog systems are becoming increasingly popular in our lives, more realistic tasks have been proposed and explored. However, new practical challenges arise. For instance, current dialog systems cannot effectively handle multiple search results when querying a database, due to the lack of such scenario...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	271,767
1807.03083	Evaluating Active Learning Heuristics for Sequential Diagnosis	Given a malfunctioning system, sequential diagnosis aims at identifying the root cause of the failure in terms of abnormally behaving system components. As initial system observations usually do not suffice to deterministically pin down just one explanation of the system's misbehavior, additional system measurements ca...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	102,420
2102.01197	Common Randomness Generation over Slow Fading Channels	This paper analyzes the problem of common randomness (CR) generation from correlated discrete sources aided by unidirectional communication over Single-Input Single-Output (SISO) slow fading channels with additive white Gaussian noise (AWGN) and arbitrary state distribution. Slow fading channels are practically relevan...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	218,023
2208.06497	SeeSaw: Interactive Ad-hoc Search Over Image Databases	As image datasets become ubiquitous, the problem of ad-hoc searches over image data is increasingly important. Many high-level data tasks in machine learning, such as constructing datasets for training and testing object detectors, imply finding ad-hoc objects or scenes within large image datasets as a key sub-problem....	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	312,732
1910.06514	Target-Oriented Deformation of Visual-Semantic Embedding Space	Multimodal embedding is a crucial research topic for cross-modal understanding, data mining, and translation. Many studies have attempted to extract representations from given entities and align them in a shared embedding space. However, because entities in different modalities exhibit different abstraction levels and ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	149,360
2204.03428	Detecting Vocal Fatigue with Neural Embeddings	Vocal fatigue refers to the feeling of tiredness and weakness of voice due to extended utilization. This paper investigates the effectiveness of neural embeddings for the detection of vocal fatigue. We compare x-vectors, ECAPA-TDNN, and wav2vec 2.0 embeddings on a corpus of academic spoken English. Low-dimensional mapp...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	290,299
2302.07027	AdapterSoup: Weight Averaging to Improve Generalization of Pretrained Language Models	Pretrained language models (PLMs) are trained on massive corpora, but often need to specialize to specific domains. A parameter-efficient adaptation method suggests training an adapter for each domain on the task of language modeling. This leads to good in-domain scores but can be impractical for domain- or resource-re...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	345,607
2206.06221	A Unified Approach for Dynamic Analysis of Tensegrity Structures with Arbitrary Rigid Bodies and Rigid Bars	This paper proposes a unified approach for dynamic modeling and simulations of general tensegrity structures with rigid bars and rigid bodies of arbitrary shapes. The natural coordinates are adopted as a non-minimal description in terms of different combinations of basic points and base vectors to resolve the heterogen...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	302,295
2407.08513	Fine-Tuning Stable Diffusion XL for Stylistic Icon Generation: A Comparison of Caption Size	In this paper, we show different fine-tuning methods for Stable Diffusion XL; this includes inference steps, and caption customization for each image to align with generating images in the style of a commercial 2D icon training set. We also show how important it is to properly define what "high-quality" really is espec...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	472,194
2005.08343	Facial Action Unit Detection using 3D Facial Landmarks	In this paper, we propose to detect facial action units (AU) using 3D facial landmarks. Specifically, we train a 2D convolutional neural network (CNN) on 3D facial landmarks, tracked using a shape index-based statistical shape model, for binary and multi-class AU detection. We show that the proposed approach is able to...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	177,587
1805.05426	O.D.E.S. : An Online Dynamic Examination System based on a CMS Wordpress plugin	This paper describes the online dynamic examination application plugin named O.D.E.S., developed according to the open source software philosophy, where the CMS Wordpress is used as programmers/coders are given the potential to develop applications from scratch with safety and ease. In ODES application there exists two...	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	false	97,421
1505.00835	A novel plasticity rule can explain the development of sensorimotor intelligence	Grounding autonomous behavior in the nervous system is a fundamental challenge for neuroscience. In particular, the self-organized behavioral development provides more questions than answers. Are there special functional units for curiosity, motivation, and creativity? This paper argues that these features can be groun...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	42,771
1603.00939	Routing Autonomous Vehicles in Congested Transportation Networks: Structural Properties and Coordination Algorithms	This paper considers the problem of routing and rebalancing a shared fleet of autonomous (i.e., self-driving) vehicles providing on-demand mobility within a capacitated transportation network, where congestion might disrupt throughput. We model the problem within a network flow framework and show that under relatively ...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	52,832
2109.05385	On the Initial Behavior Monitoring Issues in Federated Learning	In Federated Learning (FL), a group of workers participate to build a global model under the coordination of one node, the chief. Regarding the cybersecurity of FL, some attacks aim at injecting the fabricated local model updates into the system. Some defenses are based on malicious worker detection and behavioral patt...	false	false	false	false	true	false	true	false	false	false	false	false	true	false	false	false	false	false	254,770
2501.12524	Efficient Lung Ultrasound Severity Scoring Using Dedicated Feature Extractor	With the advent of the COVID-19 pandemic, ultrasound imaging has emerged as a promising technique for COVID-19 detection, due to its non-invasive nature, affordability, and portability. In response, researchers have focused on developing AI-based scoring systems to provide real-time diagnostic support. However, the lim...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	526,344
2411.13975	Transforming Static Images Using Generative Models for Video Salient Object Detection	In many video processing tasks, leveraging large-scale image datasets is a common strategy, as image data is more abundant and facilitates comprehensive knowledge transfer. A typical approach for simulating video from static images involves applying spatial transformations, such as affine transformations and spline war...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	509,993
2309.10324	Metastatic Breast Cancer Prognostication Through Multimodal Integration of Dimensionality Reduction Algorithms and Classification Algorithms	Machine learning (ML) is a branch of Artificial Intelligence (AI) where computers analyze data and find patterns in the data. The study focuses on the detection of metastatic cancer using ML. Metastatic cancer is the point where the cancer has spread to other parts of the body and is the cause of approximately 90% of c...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	392,963
1304.5213	Carbon Dating The Web: Estimating the Age of Web Resources	In the course of web research it is often necessary to estimate the creation datetime for web resources (in the general case, this value can only be estimated). While it is feasible to manually establish likely datetime values for small numbers of resources, this becomes infeasible if the collection is large. We presen...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	true	24,067
2107.03663	Graph and Recurrent Neural Network-based Vehicle Trajectory Prediction For Highway Driving	Integrating trajectory prediction to the decision-making and planning modules of modular autonomous driving systems is expected to improve the safety and efficiency of self-driving vehicles. However, a vehicle's future trajectory prediction is a challenging task since it is affected by the social interactive behaviors ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	245,221
2203.02128	Distributionally Robust Bayesian Optimization with $\varphi$-divergences	The study of robustness has received much attention due to its inevitability in data-driven settings where many systems face uncertainty. One such example of concern is Bayesian Optimization (BO), where uncertainty is multi-faceted, yet there only exists a limited number of works dedicated to this direction. In particu...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	283,650
2111.12994	NomMer: Nominate Synergistic Context in Vision Transformer for Visual Recognition	Recently, Vision Transformers (ViT), with the self-attention (SA) as the de facto ingredients, have demonstrated great potential in the computer vision community. For the sake of trade-off between efficiency and performance, a group of works merely perform SA operation within local patches, whereas the global contextua...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	268,159
1907.02911	Weight-space symmetry in deep networks gives rise to permutation saddles, connected by equal-loss valleys across the loss landscape	The permutation symmetry of neurons in each layer of a deep neural network gives rise not only to multiple equivalent global minima of the loss function, but also to first-order saddle points located on the path between the global minima. In a network of $d-1$ hidden layers with $n_k$ neurons in layers $k = 1, \ldots, ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	137,719
2408.09162	Zero-Shot Object-Centric Representation Learning	The goal of object-centric representation learning is to decompose visual scenes into a structured representation that isolates the entities. Recent successes have shown that object-centric representation learning can be scaled to real-world scenes by utilizing pre-trained self-supervised features. However, so far, obj...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	481,312
2007.13876	Semi-Supervised Learning with Data Augmentation for End-to-End ASR	In this paper, we apply Semi-Supervised Learning (SSL) along with Data Augmentation (DA) for improving the accuracy of End-to-End ASR. We focus on the consistency regularization principle, which has been successfully applied to image classification tasks, and present sequence-to-sequence (seq2seq) versions of the FixMa...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	189,243
2103.09755	Aggregated Multi-GANs for Controlled 3D Human Motion Prediction	Human motion prediction from historical pose sequence is at the core of many applications in machine intelligence. However, in current state-of-the-art methods, the predicted future motion is confined within the same activity. One can neither generate predictions that differ from the current activity, nor manipulate th...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	225,253
1901.02787	On Secure Network Coding for Multiple Unicast Traffic	This paper investigates the problem of secure communication in a wireline noiseless scenario where a source wishes to communicate to a number of destinations in the presence of a passive external adversary. Different from the multicast scenario, where all destinations are interested in receiving the same message, in th...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	118,278
2401.05882	Extreme Value Theory Based Rate Selection for Ultra-Reliable Communications	Ultra-reliable low latency communication (URLLC) requires the packet error rate to be on the order of $10^{-9}$-$10^{-5}$. Determining the appropriate transmission rate to satisfy this ultra-reliability constraint requires deriving the statistics of the channel in the ultra-reliable region and then incorporating these ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	420,945
2204.10362	Human Preferences as Dueling Bandits	The dramatic improvements in core information retrieval tasks engendered by neural rankers create a need for novel evaluation methods. If every ranker returns highly relevant items in the top ranks, it becomes difficult to recognize meaningful differences between them and to build reusable test collections. Several rec...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	292,753
2406.12319	The Comparative Trap: Pairwise Comparisons Amplifies Biased Preferences of LLM Evaluators	As large language models (LLMs) are increasingly used as evaluators for natural language generation tasks, ensuring unbiased assessments is essential. However, LLM evaluators often display biased preferences, such as favoring verbosity and authoritative tones. Our empirical analysis reveals that these biases are exacer...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	465,342
1312.1020	High-quality Image Restoration from Partial Mixed Adaptive-Random Measurements	A novel framework to construct an efficient sensing (measurement) matrix, called mixed adaptive-random (MAR) matrix, is introduced for directly acquiring a compressed image representation. The mixed sampling (sensing) procedure hybridizes adaptive edge measurements extracted from a low-resolution image with uniform ran...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	28,830
1812.07909	An Empirical Study of Generative Models with Encoders	Generative adversarial networks (GANs) are capable of producing high quality image samples. However, unlike variational autoencoders (VAEs), GANs lack encoders that provide the inverse mapping for the generators, i.e., encode images back to the latent space. In this work, we consider adversarially learned generative mo...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	116,905
2411.17711	AnyECG: Foundational Models for Electrocardiogram Analysis	Electrocardiogram (ECG), a non-invasive and affordable tool for cardiac monitoring, is highly sensitive in detecting acute heart attacks. However, due to the lengthy nature of ECG recordings, numerous machine learning methods have been developed for automated heart disease detection to reduce human workload. Despite th...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	511,559
2011.08018	High-level Prior-based Loss Functions for Medical Image Segmentation: A Survey	Today, deep convolutional neural networks (CNNs) have demonstrated state of the art performance for supervised medical image segmentation, across various imaging modalities and tasks. Despite early success, segmentation networks may still generate anatomically aberrant segmentations, with holes or inaccuracies near the...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	206,748
2004.08994	Adversarial Training for Large Neural Language Models	Generalization and robustness are both key desiderata for designing machine learning methods. Adversarial training can enhance robustness, but past work often finds it hurts generalization. In natural language processing (NLP), pre-training large neural language models such as BERT have demonstrated impressive gain in ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	173,226
2212.09981	Benchmarking person re-identification datasets and approaches for practical real-world implementations	Recently, Person Re-Identification (Re-ID) has received a lot of attention. Large datasets containing labeled images of various individuals have been released, allowing researchers to develop and test many successful approaches. However, when such Re-ID models are deployed in new cities or environments, the task of sea...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	337,282
2109.04641	Learning to Teach with Student Feedback	Knowledge distillation (KD) has gained much attention due to its effectiveness in compressing large-scale pre-trained models. In typical KD methods, the small student model is trained to match the soft targets generated by the big teacher model. However, the interaction between student and teacher is one-way. The teach...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	254,477
1906.05497	Deep Network Approximation Characterized by Number of Neurons	This paper quantitatively characterizes the approximation power of deep feed-forward neural networks (FNNs) in terms of the number of neurons. It is shown by construction that ReLU FNNs with width $\mathcal{O}\big(\max\{d\lfloor N^{1/d}\rfloor,\, N+1\}\big)$ and depth $\mathcal{O}(L)$ can approximate an arbitrary H\"ol...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	135,047
1903.00710	Lie-algebraic connections between two classes of risk-sensitive performance criteria for linear quantum stochastic systems	This paper is concerned with the original risk-sensitive performance criterion for quantum stochastic systems and its recent quadratic-exponential counterpart. These functionals are of different structure because of the noncommutativity of quantum variables and have their own useful features such as tractability of evo...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	123,076

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