Split (2)

train · 433k rows

id stringlengths 9 16	title stringlengths 4 278	abstract stringlengths 3 4.08k	cs.HC bool 2 classes	cs.CE bool 2 classes	cs.SD bool 2 classes	cs.SI bool 2 classes	cs.AI bool 2 classes	cs.IR bool 2 classes	cs.LG bool 2 classes	cs.RO bool 2 classes	cs.CL bool 2 classes	cs.IT bool 2 classes	cs.SY bool 2 classes	cs.CV bool 2 classes	cs.CR bool 2 classes	cs.CY bool 2 classes	cs.MA bool 2 classes	cs.NE bool 2 classes	cs.DB bool 2 classes	Other bool 2 classes	__index_level_0__ int64 0 541k
2102.08355	Adversarial Targeted Forgetting in Regularization and Generative Based Continual Learning Models	Continual (or "incremental") learning approaches are employed when additional knowledge or tasks need to be learned from subsequent batches or from streaming data. However these approaches are typically adversary agnostic, i.e., they do not consider the possibility of a malicious attack. In our prior work, we explored ...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	220,422
1104.2285	Elimination of Specular reflection and Identification of ROI: The First Step in Automated Detection of Cervical Cancer using Digital Colposcopy	Cervical Cancer is one of the most common forms of cancer in women worldwide. Most cases of cervical cancer can be prevented through screening programs aimed at detecting precancerous lesions. During Digital Colposcopy, Specular Reflections (SR) appear as bright spots heavily saturated with white light. These occur due...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	9,966
2201.12745	Approximate Bayesian Computation Based on Maxima Weighted Isolation Kernel Mapping	Motivation: A branching processes model yields an unevenly stochastically distributed dataset that consists of sparse and dense regions. This work addresses the problem of precisely evaluating parameters for such a model. Applying a branching processes model to an area such as cancer cell evolution faces a number of ob...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	277,768
2402.16347	CodeS: Towards Building Open-source Language Models for Text-to-SQL	Language models have shown promising performance on the task of translating natural language questions into SQL queries (Text-to-SQL). However, most of the state-of-the-art (SOTA) approaches rely on powerful yet closed-source large language models (LLMs), such as ChatGPT and GPT-4, which may have the limitations of unc...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	false	432,525
1810.09733	OCAPIS: R package for Ordinal Classification And Preprocessing In Scala	Ordinal Data are those where a natural order exist between the labels. The classification and pre-processing of this type of data is attracting more and more interest in the area of machine learning, due to its presence in many common problems. Traditionally, ordinal classification problems have been approached as nomi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	111,119
2402.03251	CLIP Can Understand Depth	Recent studies on generalizing CLIP for monocular depth estimation reveal that CLIP pre-trained on web-crawled data is inefficient for deriving proper similarities between image patches and depth-related prompts. In this paper, we adapt CLIP for meaningful quality of monocular depth estimation with dense prediction, wi...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	426,925
2107.04971	Self-service Data Classification Using Interactive Visualization and Interpretable Machine Learning	Machine learning algorithms often produce models considered as complex black-box models by both end users and developers. They fail to explain the model in terms of the domain they are designed for. The proposed Iterative Visual Logical Classifier (IVLC) is an interpretable machine learning algorithm that allows end us...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	245,620
2408.00083	Localized Gaussian Splatting Editing with Contextual Awareness	Recent text-guided generation of individual 3D object has achieved great success using diffusion priors. However, these methods are not suitable for object insertion and replacement tasks as they do not consider the background, leading to illumination mismatches within the environment. To bridge the gap, we introduce a...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	477,698
2110.00587	Sentiment and structure in word co-occurrence networks on Twitter	We explore the relationship between context and happiness scores in political tweets using word co-occurrence networks, where nodes in the network are the words, and the weight of an edge is the number of tweets in the corpus for which the two connected words co-occur. In particular, we consider tweets with hashtags #i...	false	false	false	true	false	false	false	false	true	false	false	false	false	true	false	false	false	false	258,446
2005.01177	Tailoring and Evaluating the Wikipedia for in-Domain Comparable Corpora Extraction	We propose an automatic language-independent graph-based method to build \`a-la-carte article collections on user-defined domains from the Wikipedia. The core model is based on the exploration of the encyclopaedia's category graph and can produce both monolingual and multilingual comparable collections. We run thorough...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	175,508
1104.3152	Polyethism in a colony of artificial ants	We explore self-organizing strategies for role assignment in a foraging task carried out by a colony of artificial agents. Our strategies are inspired by various mechanisms of division of labor (polyethism) observed in eusocial insects like ants, termites, or bees. Specifically we instantiate models of caste polyethism...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	10,002
cmp-lg/9411025	Multi-Dimensional Inheritance	In this paper, we present an alternative approach to multiple inheritance for typed feature structures. In our approach, a feature structure can be associated with several types coming from different hierarchies (dimensions). In case of multiple inheritance, a type has supertypes from different hierarchies. We contrast...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	536,241
1103.5120	Emergence of scale-free leadership structure in social recommender systems	The study of the organization of social networks is important for understanding of opinion formation, rumor spreading, and the emergence of trends and fashion. This paper reports empirical analysis of networks extracted from four leading sites with social functionality (Delicious, Flickr, Twitter and YouTube) and shows...	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	false	9,765
1905.00851	Lifting Vectorial Variational Problems: A Natural Formulation based on Geometric Measure Theory and Discrete Exterior Calculus	Numerous tasks in imaging and vision can be formulated as variational problems over vector-valued maps. We approach the relaxation and convexification of such vectorial variational problems via a lifting to the space of currents. To that end, we recall that functionals with polyconvex Lagrangians can be reparametrized ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	129,576
2203.12378	Long hauling eco-driving: heavy-duty trucks operational modes control with integrated road slope preview	In this paper, a complete eco-driving strategy for heavy-duty trucks (HDT) based on a finite number of driving modes with corresponding gear shifting is developed to cope with different route events and with road slope data. The problem is formulated as an optimal control problem with respect to fuel consumption and tr...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	287,251
2011.01868	Nonlinear Two-Time-Scale Stochastic Approximation: Convergence and Finite-Time Performance	Two-time-scale stochastic approximation, a generalized version of the popular stochastic approximation, has found broad applications in many areas including stochastic control, optimization, and machine learning. Despite its popularity, theoretical guarantees of this method, especially its finite-time performance, are ...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	204,739
1907.10244	AdaCoF: Adaptive Collaboration of Flows for Video Frame Interpolation	Video frame interpolation is one of the most challenging tasks in video processing research. Recently, many studies based on deep learning have been suggested. Most of these methods focus on finding locations with useful information to estimate each output pixel using their own frame warping operations. However, many o...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	139,570
2006.01284	Independent Component Analysis for Trustworthy Cyberspace during High Impact Events: An Application to Covid-19	Social media has become an important communication channel during high impact events, such as the COVID-19 pandemic. As misinformation in social media can rapidly spread, creating social unrest, curtailing the spread of misinformation during such events is a significant data challenge. While recent solutions that are b...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	179,729
2108.00089	Tensor-Train Density Estimation	Estimation of probability density function from samples is one of the central problems in statistics and machine learning. Modern neural network-based models can learn high dimensional distributions but have problems with hyperparameter selection and are often prone to instabilities during training and inference. We pr...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	248,595
2312.15356	Short-lived High-volume Multi-A(rmed)/B(andits) Testing	Modern platforms leverage randomized experiments to make informed decisions from a given set of items (``treatments''). As a particularly challenging scenario, these items may (i) arrive in high volume, with thousands of new items being released per hour, and (ii) have short lifetime, say, due to the item's transient n...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	417,983
2004.01581	Identifying highly influential travellers for spreading disease on a public transport system	The recent outbreak of a novel coronavirus and its rapid spread underlines the importance of understanding human mobility. Enclosed spaces, such as public transport vehicles (e.g. buses and trains), offer a suitable environment for infections to spread widely and quickly. Investigating the movement patterns and the phy...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	170,951
2302.08913	Referential communication in heterogeneous communities of pre-trained visual deep networks	As large pre-trained image-processing neural networks are being embedded in autonomous agents such as self-driving cars or robots, the question arises of how such systems can communicate with each other about the surrounding world, despite their different architectures and training regimes. As a first step in this dire...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	346,228
2407.19746	Octave-YOLO: Cross frequency detection network with octave convolution	Despite the rapid advancement of object detection algorithms, processing high-resolution images on embedded devices remains a significant challenge. Theoretically, the fully convolutional network architecture used in current real-time object detectors can handle all input resolutions. However, the substantial computati...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	476,908
2306.07959	Privacy Preserving Bayesian Federated Learning in Heterogeneous Settings	In several practical applications of federated learning (FL), the clients are highly heterogeneous in terms of both their data and compute resources, and therefore enforcing the same model architecture for each client is very limiting. Moreover, the need for uncertainty quantification and data privacy constraints are o...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	373,218
2104.04232	Application of blockchain for secure data transmission in distributed state estimation	The application of renewable energy sources in the power grid increases the necessity of tracking the system's state, especially in smart grids, where there is a bidirectional transfer of data and power. The complexity of coupling between communication and the electrical infrastructure in a smart grid will create a hig...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	229,333
2010.14377	Designing optimal networks for multi-commodity transport problem	Designing and optimizing different flows in networks is a relevant problem in many contexts. While a number of methods have been proposed in the physics and optimal transport literature for the one-commodity case, we lack similar results for the multi-commodity scenario. In this paper we present a model based on optima...	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	false	203,426
2109.02384	Explicit construction of the minimum error variance estimator for stochastic LTI state-space systems	In this short article, we showcase the derivation of the optimal (minimum error variance) estimator, when one part of the stochastic LTI system output is not measured but is able to be predicted from the measured system outputs. Similar derivations have been done before but not using state-space representation.	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	253,730
2212.10621	Full-Body Articulated Human-Object Interaction	Fine-grained capturing of 3D HOI boosts human activity understanding and facilitates downstream visual tasks, including action recognition, holistic scene reconstruction, and human motion synthesis. Despite its significance, existing works mostly assume that humans interact with rigid objects using only a few body part...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	337,540
2208.04505	Towards Energy-Aware Federated Learning on Battery-Powered Clients	Federated learning (FL) is a newly emerged branch of AI that facilitates edge devices to collaboratively train a global machine learning model without centralizing data and with privacy by default. However, despite the remarkable advancement, this paradigm comes with various challenges. Specifically, in large-scale dep...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	312,130
1807.09970	A Minimal Closed-Form Solution for Multi-Perspective Pose Estimation using Points and Lines	We propose a minimal solution for pose estimation using both points and lines for a multi-perspective camera. In this paper, we treat the multi-perspective camera as a collection of rigidly attached perspective cameras. These type of imaging devices are useful for several computer vision applications that require a lar...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	103,845
2306.01081	4DSR-GCN: 4D Video Point Cloud Upsampling using Graph Convolutional Networks	Time varying sequences of 3D point clouds, or 4D point clouds, are now being acquired at an increasing pace in several applications (e.g., LiDAR in autonomous or assisted driving). In many cases, such volume of data is transmitted, thus requiring that proper compression tools are applied to either reduce the resolution...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	true	370,283
1604.05358	Text-based LSTM networks for Automatic Music Composition	In this paper, we introduce new methods and discuss results of text-based LSTM (Long Short-Term Memory) networks for automatic music composition. The proposed network is designed to learn relationships within text documents that represent chord progressions and drum tracks in two case studies. In the experiments, word-...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	54,795
2102.08023	Joint self-supervised blind denoising and noise estimation	We propose a novel self-supervised image blind denoising approach in which two neural networks jointly predict the clean signal and infer the noise distribution. Assuming that the noisy observations are independent conditionally to the signal, the networks can be jointly trained without clean training data. Therefore, ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	220,319
2303.07584	An Adaptive Decision-Making Approach for Better Selection of a Blockchain Platform for Health Insurance Frauds Detection with Smart Contracts: Development and Performance Evaluation	Blockchain technology has piqued the interest of businesses of all types, while consistently improving and adapting to developers and business owners requirements. Therefore, several blockchain platforms have emerged, making it challenging to select a suitable one for a specific type of business. This paper presents a ...	false	false	false	true	false	false	false	false	false	false	false	false	true	true	false	false	false	false	351,301
1708.00577	Kernalised Multi-resolution Convnet for Visual Tracking	Visual tracking is intrinsically a temporal problem. Discriminative Correlation Filters (DCF) have demonstrated excellent performance for high-speed generic visual object tracking. Built upon their seminal work, there has been a plethora of recent improvements relying on convolutional neural network (CNN) pretrained on...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	78,229
1901.03315	Automated Synthesis of Safe Digital Controllers for Sampled-Data Stochastic Nonlinear Systems	We present a new method for the automated synthesis of digital controllers with formal safety guarantees for systems with nonlinear dynamics, noisy output measurements, and stochastic disturbances. Our method derives digital controllers such that the corresponding closed-loop system, modeled as a sampled-data stochasti...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	118,384
1204.5852	Context-sensitive Spelling Correction Using Google Web 1T 5-Gram Information	In computing, spell checking is the process of detecting and sometimes providing spelling suggestions for incorrectly spelled words in a text. Basically, a spell checker is a computer program that uses a dictionary of words to perform spell checking. The bigger the dictionary is, the higher is the error detection rate....	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	15,675
2206.01880	Learning in Congestion Games with Bandit Feedback	In this paper, we investigate Nash-regret minimization in congestion games, a class of games with benign theoretical structure and broad real-world applications. We first propose a centralized algorithm based on the optimism in the face of uncertainty principle for congestion games with (semi-)bandit feedback, and obta...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	true	300,646
2004.05884	Adversarial Weight Perturbation Helps Robust Generalization	The study on improving the robustness of deep neural networks against adversarial examples grows rapidly in recent years. Among them, adversarial training is the most promising one, which flattens the input loss landscape (loss change with respect to input) via training on adversarially perturbed examples. However, how...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	172,349
2207.01795	PatchZero: Defending against Adversarial Patch Attacks by Detecting and Zeroing the Patch	Adversarial patch attacks mislead neural networks by injecting adversarial pixels within a local region. Patch attacks can be highly effective in a variety of tasks and physically realizable via attachment (e.g. a sticker) to the real-world objects. Despite the diversity in attack patterns, adversarial patches tend to ...	false	false	false	false	false	false	true	false	false	false	false	true	true	false	false	false	false	false	306,297
2106.08902	Adaptive Clustering and Personalization in Multi-Agent Stochastic Linear Bandits	We consider the problem of minimizing regret in an $N$ agent heterogeneous stochastic linear bandits framework, where the agents (users) are similar but not all identical. We model user heterogeneity using two popularly used ideas in practice; (i) A clustering framework where users are partitioned into groups with user...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	241,472
1908.10344	Intra-Camera Supervised Person Re-Identification: A New Benchmark	Existing person re-identification (re-id) methods rely mostly on a large set of inter-camera identity labelled training data, requiring a tedious data collection and annotation process therefore leading to poor scalability in practical re-id applications. To overcome this fundamental limitation, we consider person re-i...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	143,092
1910.12703	Deep Joint Source-Channel Coding for Wireless Image Retrieval	Motivated by surveillance applications with wireless cameras or drones, we consider the problem of image retrieval over a wireless channel. Conventional systems apply lossy compression on query images to reduce the data that must be transmitted over the bandwidth and power limited wireless link. We first note that reco...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	151,168
2304.02801	End-to-end Manipulator Calligraphy Planning via Variational Imitation Learning	Planning from demonstrations has shown promising results with the advances of deep neural networks. One of the most popular real-world applications is automated handwriting using a robotic manipulator. Classically it is simplified as a two-dimension problem. This representation is suitable for elementary drawings, but ...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	356,554
2502.06039	Benchmarking Prompt Engineering Techniques for Secure Code Generation with GPT Models	Prompt engineering reduces reasoning mistakes in Large Language Models (LLMs). However, its effectiveness in mitigating vulnerabilities in LLM-generated code remains underexplored. To address this gap, we implemented a benchmark to automatically assess the impact of various prompt engineering strategies on code securit...	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	true	531,894
1906.05261	LAEO-Net: revisiting people Looking At Each Other in videos	Capturing the `mutual gaze' of people is essential for understanding and interpreting the social interactions between them. To this end, this paper addresses the problem of detecting people Looking At Each Other (LAEO) in video sequences. For this purpose, we propose LAEO-Net, a new deep CNN for determining LAEO in vid...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	134,974
2101.04645	Double-Adversarial Activation Anomaly Detection: Adversarial Autoencoders are Anomaly Generators	Anomaly detection is a challenging task for machine learning algorithms due to the inherent class imbalance. It is costly and time-demanding to manually analyse the observed data, thus usually only few known anomalies if any are available. Inspired by generative models and the analysis of the hidden activations of neur...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	215,196
2112.07111	EMDS-6: Environmental Microorganism Image Dataset Sixth Version for Image Denoising, Segmentation, Feature Extraction, Classification and Detection Methods Evaluation	Environmental microorganisms (EMs) are ubiquitous around us and have an important impact on the survival and development of human society. However, the high standards and strict requirements for the preparation of environmental microorganism (EM) data have led to the insufficient of existing related databases, not to m...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	271,380
1804.01661	Learning Strict Identity Mappings in Deep Residual Networks	A family of super deep networks, referred to as residual networks or ResNet, achieved record-beating performance in various visual tasks such as image recognition, object detection, and semantic segmentation. The ability to train very deep networks naturally pushed the researchers to use enormous resources to achieve t...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	94,266
1810.07652	Fine-tuning on Clean Data for End-to-End Speech Translation: FBK @ IWSLT 2018	This paper describes FBK's submission to the end-to-end English-German speech translation task at IWSLT 2018. Our system relies on a state-of-the-art model based on LSTMs and CNNs, where the CNNs are used to reduce the temporal dimension of the audio input, which is in general much higher than machine translation input...	false	false	true	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	110,675
2307.03115	KoRC: Knowledge oriented Reading Comprehension Benchmark for Deep Text Understanding	Deep text understanding, which requires the connections between a given document and prior knowledge beyond its text, has been highlighted by many benchmarks in recent years. However, these benchmarks have encountered two major limitations. On the one hand, most of them require human annotation of knowledge, which lead...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	377,928
1801.01442	ObamaNet: Photo-realistic lip-sync from text	We present ObamaNet, the first architecture that generates both audio and synchronized photo-realistic lip-sync videos from any new text. Contrary to other published lip-sync approaches, ours is only composed of fully trainable neural modules and does not rely on any traditional computer graphics methods. More precisel...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	87,725
1809.02244	Learning Optimal Fair Policies	Systematic discriminatory biases present in our society influence the way data is collected and stored, the way variables are defined, and the way scientific findings are put into practice as policy. Automated decision procedures and learning algorithms applied to such data may serve to perpetuate existing injustice or...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	107,001
2303.17123	Masked and Adaptive Transformer for Exemplar Based Image Translation	We present a novel framework for exemplar based image translation. Recent advanced methods for this task mainly focus on establishing cross-domain semantic correspondence, which sequentially dominates image generation in the manner of local style control. Unfortunately, cross-domain semantic matching is challenging; an...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	355,113
1802.10519	On the Lie bracket approximation approach to distributed optimization: Extensions and limitations	We consider the problem of solving a smooth convex optimization problem with equality and inequality constraints in a distributed fashion. Assuming that we have a group of agents available capable of communicating over a communication network described by a time-invariant directed graph, we derive distributed continuou...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	91,544
2203.06359	Self-Sustaining Representation Expansion for Non-Exemplar Class-Incremental Learning	Non-exemplar class-incremental learning is to recognize both the old and new classes when old class samples cannot be saved. It is a challenging task since representation optimization and feature retention can only be achieved under supervision from new classes. To address this problem, we propose a novel self-sustaini...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	285,084
2406.06748	Starling Formation-Flying Optical Experiment: Initial Operations and Flight Results	This paper presents initial flight results for distributed optical angles-only navigation of a swarm of small spacecraft, conducted during the Starling Formation-Flying Optical Experiment (StarFOX). StarFOX is a core payload of the NASA Starling mission, which consists of four CubeSats launched in 2023. Prior angles-on...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	462,753
2109.08958	AutoInit: Analytic Signal-Preserving Weight Initialization for Neural Networks	Neural networks require careful weight initialization to prevent signals from exploding or vanishing. Existing initialization schemes solve this problem in specific cases by assuming that the network has a certain activation function or topology. It is difficult to derive such weight initialization strategies, and mode...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	256,094
2210.03269	Multi-agent Deep Covering Skill Discovery	The use of skills (a.k.a., options) can greatly accelerate exploration in reinforcement learning, especially when only sparse reward signals are available. While option discovery methods have been proposed for individual agents, in multi-agent reinforcement learning settings, discovering collaborative options that can ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	321,961
2305.12635	A bioinspired three-stage model for camouflaged object detection	Camouflaged objects are typically assimilated into their backgrounds and exhibit fuzzy boundaries. The complex environmental conditions and the high intrinsic similarity between camouflaged targets and their surroundings pose significant challenges in accurately locating and segmenting these objects in their entirety. ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	366,099
2203.09138	MuKEA: Multimodal Knowledge Extraction and Accumulation for Knowledge-based Visual Question Answering	Knowledge-based visual question answering requires the ability of associating external knowledge for open-ended cross-modal scene understanding. One limitation of existing solutions is that they capture relevant knowledge from text-only knowledge bases, which merely contain facts expressed by first-order predicates or ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	286,045
2209.13997	A Review of Modern Approaches for Coronary Angiography Imaging Analysis	Coronary Heart Disease (CHD) is a leading cause of death in the modern world. The development of modern analytical tools for diagnostics and treatment of CHD is receiving substantial attention from the scientific community. Deep learning-based algorithms, such as segmentation networks and detectors, play an important r...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	320,098
2404.00925	LLMs are Good Sign Language Translators	Sign Language Translation (SLT) is a challenging task that aims to translate sign videos into spoken language. Inspired by the strong translation capabilities of large language models (LLMs) that are trained on extensive multilingual text corpora, we aim to harness off-the-shelf LLMs to handle SLT. In this paper, we re...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	443,172
2206.15217	Implicit U-Net for volumetric medical image segmentation	U-Net has been the go-to architecture for medical image segmentation tasks, however computational challenges arise when extending the U-Net architecture to 3D images. We propose the Implicit U-Net architecture that adapts the efficient Implicit Representation paradigm to supervised image segmentation tasks. By combinin...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	305,525
2101.02931	Block-Term Tensor Decomposition Model Selection and Computation: The Bayesian Way	The so-called block-term decomposition (BTD) tensor model, especially in its rank-$(L_r,L_r,1)$ version, has been recently receiving increasing attention due to its enhanced ability of representing systems and signals that are composed of \emph{blocks} of rank higher than one, a scenario encountered in numerous and div...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	214,776
1904.04794	CMIR-NET : A Deep Learning Based Model For Cross-Modal Retrieval In Remote Sensing	We address the problem of cross-modal information retrieval in the domain of remote sensing. In particular, we are interested in two application scenarios: i) cross-modal retrieval between panchromatic (PAN) and multi-spectral imagery, and ii) multi-label image retrieval between very high resolution (VHR) images and sp...	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	127,129
2203.15916	Current Implicit Policies May Not Eradicate COVID-19	Successful predictive modeling of epidemics requires an understanding of the implicit feedback control strategies which are implemented by populations to modulate the spread of contagion. While this task of capturing endogenous behavior can be achieved through intricate modeling assumptions, we find that a population's...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	288,581
1908.08992	MEx: Multi-modal Exercises Dataset for Human Activity Recognition	MEx: Multi-modal Exercises Dataset is a multi-sensor, multi-modal dataset, implemented to benchmark Human Activity Recognition(HAR) and Multi-modal Fusion algorithms. Collection of this dataset was inspired by the need for recognising and evaluating quality of exercise performance to support patients with Musculoskelet...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	142,712
2002.09046	Neural Bayes: A Generic Parameterization Method for Unsupervised Representation Learning	We introduce a parameterization method called Neural Bayes which allows computing statistical quantities that are in general difficult to compute and opens avenues for formulating new objectives for unsupervised representation learning. Specifically, given an observed random variable $\mathbf{x}$ and a latent discrete ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	164,944
1710.10177	Combining Aspects of Genetic Algorithms with Weighted Recommender Hybridization	Recommender systems are established means to inspire users to watch interesting movies, discover baby names, or read books. The recommendation quality further improves by combining the results of multiple recommendation algorithms using hybridization methods. In this paper, we focus on the task of combining unscored re...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	83,323
2412.17092	SAIL: Sample-Centric In-Context Learning for Document Information Extraction	Document Information Extraction (DIE) aims to extract structured information from Visually Rich Documents (VRDs). Previous full-training approaches have demonstrated strong performance but may struggle with generalization to unseen data. In contrast, training-free methods leverage powerful pre-trained models like Large...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	519,826
1109.0631	LWE-based Identification Schemes	Some hard problems from lattices, like LWE (Learning with Errors), are particularly suitable for application in Cryptography due to the possibility of using worst-case to average-case reductions as evidence of strong security properties. In this work, we show two LWE-based constructions of zero-knowledge identification...	false	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	11,952
2201.01787	Does Entity Abstraction Help Generative Transformers Reason?	We study the utility of incorporating entity type abstractions into pre-trained Transformers and test these methods on four NLP tasks requiring different forms of logical reasoning: (1) compositional language understanding with text-based relational reasoning (CLUTRR), (2) abductive reasoning (ProofWriter), (3) multi-h...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	274,348
1206.3559	Real time facial expression recognition using a novel method	This paper discusses a novel method for Facial Expression Recognition System which performs facial expression analysis in a near real time from a live web cam feed. Primary objectives were to get results in a near real time with light invariant, person independent and pose invariant way. The system is composed of two d...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	16,575
1901.04630	Deep Learning-Aided Trainable Projected Gradient Decoding for LDPC Codes	We present a novel optimization-based decoding algorithm for LDPC codes that is suitable for hardware architectures specialized to feed-forward neural networks. The algorithm is based on the projected gradient descent algorithm with a penalty function for solving a non-convex minimization problem. The proposed algorith...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	118,632
1608.07738	Testing APSyn against Vector Cosine on Similarity Estimation	In Distributional Semantic Models (DSMs), Vector Cosine is widely used to estimate similarity between word vectors, although this measure was noticed to suffer from several shortcomings. The recent literature has proposed other methods which attempt to mitigate such biases. In this paper, we intend to investigate APSyn...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	60,263
2401.09885	Source Code Clone Detection Using Unsupervised Similarity Measures	Assessing similarity in source code has gained significant attention in recent years due to its importance in software engineering tasks such as clone detection and code search and recommendation. This work presents a comparative analysis of unsupervised similarity measures for identifying source code clone detection. ...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	true	422,418
1911.05636	Prevalence of code mixing in semi-formal patient communication in low resource languages of South Africa	In this paper we address the problem of code-mixing in resource-poor language settings. We examine data consisting of 182k unique questions generated by users of the MomConnect helpdesk, part of a national scale public health platform in South Africa. We show evidence of code-switching at the level of approximately 10%...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	153,325
2406.11200	AvaTaR: Optimizing LLM Agents for Tool Usage via Contrastive Reasoning	Large language model (LLM) agents have demonstrated impressive capabilities in utilizing external tools and knowledge to boost accuracy and reduce hallucinations. However, developing prompting techniques that enable LLM agents to effectively use these tools and knowledge remains a heuristic and labor-intensive task. He...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	464,764
1910.01269	Learning Point Embeddings from Shape Repositories for Few-Shot Segmentation	User generated 3D shapes in online repositories contain rich information about surfaces, primitives, and their geometric relations, often arranged in a hierarchy. We present a framework for learning representations of 3D shapes that reflect the information present in this meta data and show that it leads to improved ge...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	147,892
2307.02227	MAE-DFER: Efficient Masked Autoencoder for Self-supervised Dynamic Facial Expression Recognition	Dynamic facial expression recognition (DFER) is essential to the development of intelligent and empathetic machines. Prior efforts in this field mainly fall into supervised learning paradigm, which is severely restricted by the limited labeled data in existing datasets. Inspired by recent unprecedented success of maske...	true	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	true	377,625
2102.00697	Zero-Error Sum Modulo Two with a Common Observation	This paper investigates the classical modulo two sum problem in source coding, but with a common observation: a transmitter observes $(X,Z)$, the other transmitter observes $(Y,Z)$, and the receiver wants to compute $X \oplus Y$ without error. Through a coupling argument, this paper establishes a new lower bound on the...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	217,885
1906.09211	Universal Approximation of Input-Output Maps by Temporal Convolutional Nets	There has been a recent shift in sequence-to-sequence modeling from recurrent network architectures to convolutional network architectures due to computational advantages in training and operation while still achieving competitive performance. For systems having limited long-term temporal dependencies, the approximatio...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	136,080
1805.10723	Designing for Democratization: Introducing Novices to Artificial Intelligence Via Maker Kits	Existing research highlight the myriad of benefits realized when technology is sufficiently democratized and made accessible to non-technical or novice users. However, democratizing complex technologies such as artificial intelligence (AI) remains hard. In this work, we draw on theoretical underpinnings from the democr...	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	98,752
2111.07640	AnimeCeleb: Large-Scale Animation CelebHeads Dataset for Head Reenactment	We present a novel Animation CelebHeads dataset (AnimeCeleb) to address an animation head reenactment. Different from previous animation head datasets, we utilize 3D animation models as the controllable image samplers, which can provide a large amount of head images with their corresponding detailed pose annotations. T...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	266,441
1708.02444	Scheduling and Power Control for V2V Broadcast Communications with Co-Channel and Adjacent Channel Interference	This paper investigates how to mitigate the impact of both co-channel interference and adjacent channel interference (ACI) on vehicle-to-vehicle (V2V) broadcast communication by scheduling and power control. The optimal joint scheduling and power control problem, with the objective to maximize the number of connected v...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	78,591
2410.16124	MNIST-Nd: a set of naturalistic datasets to benchmark clustering across dimensions	Driven by advances in recording technology, large-scale high-dimensional datasets have emerged across many scientific disciplines. Especially in biology, clustering is often used to gain insights into the structure of such datasets, for instance to understand the organization of different cell types. However, clusterin...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	500,884
2207.05483	CorrI2P: Deep Image-to-Point Cloud Registration via Dense Correspondence	Motivated by the intuition that the critical step of localizing a 2D image in the corresponding 3D point cloud is establishing 2D-3D correspondence between them, we propose the first feature-based dense correspondence framework for addressing the image-to-point cloud registration problem, dubbed CorrI2P, which consists...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	307,558
2402.18866	Dr. Strategy: Model-Based Generalist Agents with Strategic Dreaming	Model-based reinforcement learning (MBRL) has been a primary approach to ameliorating the sample efficiency issue as well as to make a generalist agent. However, there has not been much effort toward enhancing the strategy of dreaming itself. Therefore, it is a question whether and how an agent can "dream better" in a ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	433,595
1802.05477	Approximate quantum Markov chains	This book is an introduction to quantum Markov chains and explains how this concept is connected to the question of how well a lost quantum mechanical system can be recovered from a correlated subsystem. To achieve this goal, we strengthen the data-processing inequality such that it reveals a statement about the recons...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	90,450
1901.06808	Online Learning for Measuring Incentive Compatibility in Ad Auctions	In this paper we investigate the problem of measuring end-to-end Incentive Compatibility (IC) regret given black-box access to an auction mechanism. Our goal is to 1) compute an estimate for IC regret in an auction, 2) provide a measure of certainty around the estimate of IC regret, and 3) minimize the time it takes to...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	119,095
1905.12776	Beyond Online Balanced Descent: An Optimal Algorithm for Smoothed Online Optimization	We study online convex optimization in a setting where the learner seeks to minimize the sum of a per-round hitting cost and a movement cost which is incurred when changing decisions between rounds. We prove a new lower bound on the competitive ratio of any online algorithm in the setting where the costs are $m$-strong...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	132,871
2007.07115	Estimation of Thermodynamic Observables in Lattice Field Theories with Deep Generative Models	In this work, we demonstrate that applying deep generative machine learning models for lattice field theory is a promising route for solving problems where Markov Chain Monte Carlo (MCMC) methods are problematic. More specifically, we show that generative models can be used to estimate the absolute value of the free en...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	187,232
2111.01361	Outlier-Robust Optimal Transport: Duality, Structure, and Statistical Analysis	The Wasserstein distance, rooted in optimal transport (OT) theory, is a popular discrepancy measure between probability distributions with various applications to statistics and machine learning. Despite their rich structure and demonstrated utility, Wasserstein distances are sensitive to outliers in the considered dis...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	264,531
2411.02523	Evaluating the Impact of Lab Test Results on Large Language Models Generated Differential Diagnoses from Clinical Case Vignettes	Differential diagnosis is crucial for medicine as it helps healthcare providers systematically distinguish between conditions that share similar symptoms. This study assesses the impact of lab test results on differential diagnoses (DDx) made by large language models (LLMs). Clinical vignettes from 50 case reports from...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	505,533
2405.14294	Tuning-free Universally-Supervised Semantic Segmentation	This work presents a tuning-free semantic segmentation framework based on classifying SAM masks by CLIP, which is universally applicable to various types of supervision. Initially, we utilize CLIP's zero-shot classification ability to generate pseudo-labels or perform open-vocabulary segmentation. However, the misalign...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	456,356
2309.07315	Traveling Words: A Geometric Interpretation of Transformers	Transformers have significantly advanced the field of natural language processing, but comprehending their internal mechanisms remains a challenge. In this paper, we introduce a novel geometric perspective that elucidates the inner mechanisms of transformer operations. Our primary contribution is illustrating how layer...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	391,731
2302.13033	Speaker Recognition in Realistic Scenario Using Multimodal Data	In recent years, an association is established between faces and voices of celebrities leveraging large scale audio-visual information from YouTube. The availability of large scale audio-visual datasets is instrumental in developing speaker recognition methods based on standard Convolutional Neural Networks. Thus, the ...	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	347,786
1708.03278	Motion Feature Augmented Recurrent Neural Network for Skeleton-based Dynamic Hand Gesture Recognition	Dynamic hand gesture recognition has attracted increasing interests because of its importance for human computer interaction. In this paper, we propose a new motion feature augmented recurrent neural network for skeleton-based dynamic hand gesture recognition. Finger motion features are extracted to describe finger mov...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	78,743
1903.03614	Gradient Descent based Optimization Algorithms for Deep Learning Models Training	In this paper, we aim at providing an introduction to the gradient descent based optimization algorithms for learning deep neural network models. Deep learning models involving multiple nonlinear projection layers are very challenging to train. Nowadays, most of the deep learning model training still relies on the back...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	123,775

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