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
2402.18007	Mixer is more than just a model	Recently, MLP structures have regained popularity, with MLP-Mixer standing out as a prominent example. In the field of computer vision, MLP-Mixer is noted for its ability to extract data information from both channel and token perspectives, effectively acting as a fusion of channel and token information. Indeed, Mixer ...	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	433,239
2405.06164	Skeet: Towards a Lightweight Serverless Framework Supporting Modern AI-Driven App Development	The field of web and mobile software frameworks is relatively mature, with a large variety of tools in different languages that facilitate traditional app development where data in a relational database is displayed and modified. Our position is that many current frameworks became popular during single server deploymen...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	453,192
2008.11707	Bandit Data-Driven Optimization	Applications of machine learning in the non-profit and public sectors often feature an iterative workflow of data acquisition, prediction, and optimization of interventions. There are four major pain points that a machine learning pipeline must overcome in order to be actually useful in these settings: small data, data...	false	false	false	false	true	false	true	false	false	false	false	false	false	true	false	false	false	false	193,356
2408.12673	Enhancing Transferability of Adversarial Attacks with GE-AdvGAN+: A Comprehensive Framework for Gradient Editing	Transferable adversarial attacks pose significant threats to deep neural networks, particularly in black-box scenarios where internal model information is inaccessible. Studying adversarial attack methods helps advance the performance of defense mechanisms and explore model vulnerabilities. These methods can uncover an...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	482,838
2204.00400	Probing Speech Emotion Recognition Transformers for Linguistic Knowledge	Large, pre-trained neural networks consisting of self-attention layers (transformers) have recently achieved state-of-the-art results on several speech emotion recognition (SER) datasets. These models are typically pre-trained in self-supervised manner with the goal to improve automatic speech recognition performance -...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	289,244
1904.00696	Dance with Flow: Two-in-One Stream Action Detection	The goal of this paper is to detect the spatio-temporal extent of an action. The two-stream detection network based on RGB and flow provides state-of-the-art accuracy at the expense of a large model-size and heavy computation. We propose to embed RGB and optical-flow into a single two-in-one stream network with new lay...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	125,940
1805.12253	Sequential Experimental Design for Optimal Structural Intervention in Gene Regulatory Networks Based on the Mean Objective Cost of Uncertainty	Scientists are attempting to use models of ever increasing complexity, especially in medicine, where gene-based diseases such as cancer require better modeling of cell regulation. Complex models suffer from uncertainty and experiments are needed to reduce this uncertainty. Because experiments can be costly and time-con...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	99,124
2407.11921	IPA-NeRF: Illusory Poisoning Attack Against Neural Radiance Fields	Neural Radiance Field (NeRF) represents a significant advancement in computer vision, offering implicit neural network-based scene representation and novel view synthesis capabilities. Its applications span diverse fields including robotics, urban mapping, autonomous navigation, virtual reality/augmented reality, etc.,...	false	false	false	false	false	false	false	false	false	false	false	true	true	false	false	false	false	false	473,675
2312.05461	STREAMLINE: An Automated Machine Learning Pipeline for Biomedicine Applied to Examine the Utility of Photography-Based Phenotypes for OSA Prediction Across International Sleep Centers	While machine learning (ML) includes a valuable array of tools for analyzing biomedical data, significant time and expertise is required to assemble effective, rigorous, and unbiased pipelines. Automated ML (AutoML) tools seek to facilitate ML application by automating a subset of analysis pipeline elements. In this st...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	414,094
2203.16995	Message Passing Neural Networks for Hypergraphs	Hypergraph representations are both more efficient and better suited to describe data characterized by relations between two or more objects. In this work, we present a new graph neural network based on message passing capable of processing hypergraph-structured data. We show that the proposed model defines a design sp...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	289,003
1608.08868	Demographic Dialectal Variation in Social Media: A Case Study of African-American English	Though dialectal language is increasingly abundant on social media, few resources exist for developing NLP tools to handle such language. We conduct a case study of dialectal language in online conversational text by investigating African-American English (AAE) on Twitter. We propose a distantly supervised model to ide...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	60,403
2311.12091	DAS: A Deformable Attention to Capture Salient Information in CNNs	Convolutional Neural Networks (CNNs) excel in local spatial pattern recognition. For many vision tasks, such as object recognition and segmentation, salient information is also present outside CNN's kernel boundaries. However, CNNs struggle in capturing such relevant information due to their confined receptive fields. ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	409,209
2410.03734	Accent conversion using discrete units with parallel data synthesized from controllable accented TTS	The goal of accent conversion (AC) is to convert speech accents while preserving content and speaker identity. Previous methods either required reference utterances during inference, did not preserve speaker identity well, or used one-to-one systems that could only be trained for each non-native accent. This paper pres...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	494,925
1809.09441	Temporal Relational Ranking for Stock Prediction	Stock prediction aims to predict the future trends of a stock in order to help investors to make good investment decisions. Traditional solutions for stock prediction are based on time-series models. With the recent success of deep neural networks in modeling sequential data, deep learning has become a promising choice...	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	108,712
2305.05968	Investigating Forgetting in Pre-Trained Representations Through Continual Learning	Representation forgetting refers to the drift of contextualized representations during continual training. Intuitively, the representation forgetting can influence the general knowledge stored in pre-trained language models (LMs), but the concrete effect is still unclear. In this paper, we study the effect of represent...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	363,357
2205.01297	RU-Net: Regularized Unrolling Network for Scene Graph Generation	Scene graph generation (SGG) aims to detect objects and predict the relationships between each pair of objects. Existing SGG methods usually suffer from several issues, including 1) ambiguous object representations, as graph neural network-based message passing (GMP) modules are typically sensitive to spurious inter-no...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	294,534
2010.10207	Micro CT Image-Assisted Cross Modality Super-Resolution of Clinical CT Images Utilizing Synthesized Training Dataset	This paper proposes a novel, unsupervised super-resolution (SR) approach for performing the SR of a clinical CT into the resolution level of a micro CT ($\mu$CT). The precise non-invasive diagnosis of lung cancer typically utilizes clinical CT data. Due to the resolution limitations of clinical CT (about $0.5 \times 0....	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	201,818
2306.08173	Safeguarding Data in Multimodal AI: A Differentially Private Approach to CLIP Training	The surge in multimodal AI's success has sparked concerns over data privacy in vision-and-language tasks. While CLIP has revolutionized multimodal learning through joint training on images and text, its potential to unintentionally disclose sensitive information necessitates the integration of privacy-preserving mechan...	false	false	false	false	false	false	true	false	false	true	false	false	true	false	false	false	false	false	373,305
1207.5857	Distance Distributions in Regular Polygons	This paper derives the exact cumulative density function of the distance between a randomly located node and any arbitrary reference point inside a regular $\el$-sided polygon. Using this result, we obtain the closed-form probability density function (PDF) of the Euclidean distance between any arbitrary reference point...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	17,751
2104.02821	Towards Measuring Fairness in AI: the Casual Conversations Dataset	This paper introduces a novel dataset to help researchers evaluate their computer vision and audio models for accuracy across a diverse set of age, genders, apparent skin tones and ambient lighting conditions. Our dataset is composed of 3,011 subjects and contains over 45,000 videos, with an average of 15 videos per pe...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	228,861
2407.08940	Large Language Models as Biomedical Hypothesis Generators: A Comprehensive Evaluation	The rapid growth of biomedical knowledge has outpaced our ability to efficiently extract insights and generate novel hypotheses. Large language models (LLMs) have emerged as a promising tool to revolutionize knowledge interaction and potentially accelerate biomedical discovery. In this paper, we present a comprehensive...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	472,368
2211.08480	LiePoseNet: Heterogeneous Loss Function Based on Lie Group for Significant Speed-up of PoseNet Training Process	Visual localization is an essential modern technology for robotics and computer vision. Popular approaches for solving this task are image-based methods. Nowadays, these methods have low accuracy and a long training time. The reasons are the lack of rigid-body and projective geometry awareness, landmark symmetry, and h...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	330,642
1806.08488	Optimal Design of Virtual Inertia and Damping Coefficients for Virtual Synchronous Machines	Increased penetration of inverter-connected renewable energy sources (RES) in the power system has resulted in a decrease in available rotational inertia which serves as an immediate response to frequency deviation due to disturbances. The concept of virtual inertia has been proposed to combat this decrease by enabling...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	101,163
1606.02314	NOUS: Construction and Querying of Dynamic Knowledge Graphs	The ability to construct domain specific knowledge graphs (KG) and perform question-answering or hypothesis generation is a transformative capability. Despite their value, automated construction of knowledge graphs remains an expensive technical challenge that is beyond the reach for most enterprises and academic insti...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	56,938
2502.13721	Learning Novel Transformer Architecture for Time-series Forecasting	Despite the success of Transformer-based models in the time-series prediction (TSP) tasks, the existing Transformer architecture still face limitations and the literature lacks comprehensive explorations into alternative architectures. To address these challenges, we propose AutoFormer-TS, a novel framework that levera...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	535,484
1707.07371	Integration of Information Patterns in the Modeling and Design of Mobility Management Services	Over the last decade, the rise of the mobile internet and the usage of mobile devices has enabled ubiquitous traffic information. With the increased adoption of specific smartphone applications, the number of users of routing applications has become large enough to disrupt traffic flow patterns in a significant manner....	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	77,613
cs/0307018	Universal Voting Protocol Tweaks to Make Manipulation Hard	Voting is a general method for preference aggregation in multiagent settings, but seminal results have shown that all (nondictatorial) voting protocols are manipulable. One could try to avoid manipulation by using voting protocols where determining a beneficial manipulation is hard computationally. A number of recent p...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	true	537,916
1904.09823	Ship Instance Segmentation From Remote Sensing Images Using Sequence Local Context Module	The performance of object instance segmentation in remote sensing images has been greatly improved through the introduction of many landmark frameworks based on convolutional neural network. However, the object densely issue still affects the accuracy of such segmentation frameworks. Objects of the same class are easil...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	128,491
1604.07974	Non-convexity of private capacity and classical environment-assisted capacity of a quantum channel	The capacity of classical channels is convex. This is not the case for the quantum capacity of a channel: the capacity of a mixture of different quantum channels exceeds the mixture of the individual capacities and thus is non-convex. Here we show that this effect goes beyond the quantum capacity and holds for the priv...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	55,155
2303.12914	TRON: Transformer Neural Network Acceleration with Non-Coherent Silicon Photonics	Transformer neural networks are rapidly being integrated into state-of-the-art solutions for natural language processing (NLP) and computer vision. However, the complex structure of these models creates challenges for accelerating their execution on conventional electronic platforms. We propose the first silicon photon...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	353,454
2109.02252	Generative Models Improve Radiomics Performance in Different Tasks and Different Datasets: An Experimental Study	Radiomics is an active area of research focusing on high throughput feature extraction from medical images with a wide array of applications in clinical practice, such as clinical decision support in oncology. However, noise in low dose computed tomography (CT) scans can impair the accurate extraction of radiomic featu...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	253,686
2301.07137	Heterogeneous Multi-Robot Reinforcement Learning	Cooperative multi-robot tasks can benefit from heterogeneity in the robots' physical and behavioral traits. In spite of this, traditional Multi-Agent Reinforcement Learning (MARL) frameworks lack the ability to explicitly accommodate policy heterogeneity, and typically constrain agents to share neural network parameter...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	true	false	false	false	340,838
2303.03757	Deep Learning for Inertial Positioning: A Survey	Inertial sensors are widely utilized in smartphones, drones, robots, and IoT devices, playing a crucial role in enabling ubiquitous and reliable localization. Inertial sensor-based positioning is essential in various applications, including personal navigation, location-based security, and human-device interaction. How...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	349,846
1905.06480	The CEDAR Workbench: An Ontology-Assisted Environment for Authoring Metadata that Describe Scientific Experiments	The Center for Expanded Data Annotation and Retrieval (CEDAR) aims to revolutionize the way that metadata describing scientific experiments are authored. The software we have developed--the CEDAR Workbench--is a suite of Web-based tools and REST APIs that allows users to construct metadata templates, to fill in templat...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	131,003
2401.00282	Deep Generative Symbolic Regression	Symbolic regression (SR) aims to discover concise closed-form mathematical equations from data, a task fundamental to scientific discovery. However, the problem is highly challenging because closed-form equations lie in a complex combinatorial search space. Existing methods, ranging from heuristic search to reinforceme...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	418,940
2109.14525	DRAN: Detailed Region-Adaptive Normalization for Conditional Image Synthesis	In recent years, conditional image synthesis has attracted growing attention due to its controllability in the image generation process. Although recent works have achieved realistic results, most of them have difficulty handling fine-grained styles with subtle details. To address this problem, a novel normalization mo...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	257,989
2406.18985	Exploiting Structured Sparsity in Near Field: From the Perspective of Decomposition	The structured sparsity can be leveraged in traditional far-field channels, greatly facilitating efficient sparse channel recovery by compressing the complexity of overheads to the level of the scatterer number. However, when experiencing a fundamental shift from planar-wave-based far-field modeling to spherical-wave-b...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	468,259
2209.10043	SynthA1c: Towards Clinically Interpretable Patient Representations for Diabetes Risk Stratification	Early diagnosis of Type 2 Diabetes Mellitus (T2DM) is crucial to enable timely therapeutic interventions and lifestyle modifications. As the time available for clinical office visits shortens and medical imaging data become more widely available, patient image data could be used to opportunistically identify patients f...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	318,721
1907.04201	Thompson Sampling for Combinatorial Network Optimization in Unknown Environments	Influence maximization, adaptive routing, and dynamic spectrum allocation all require choosing the right action from a large set of alternatives. Thanks to the advances in combinatorial optimization, these and many similar problems can be efficiently solved given an environment with known stochasticity. In this paper, ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	138,043
2305.18466	Test-Time Training on Nearest Neighbors for Large Language Models	Many recent efforts augment language models with retrieval, by adding retrieved data to the input context. For this approach to succeed, the retrieved data must be added at both training and test time. Moreover, as input length grows linearly with the size of retrieved data, cost in computation and memory grows quadrat...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	369,081
2112.12089	Reflash Dropout in Image Super-Resolution	Dropout is designed to relieve the overfitting problem in high-level vision tasks but is rarely applied in low-level vision tasks, like image super-resolution (SR). As a classic regression problem, SR exhibits a different behaviour as high-level tasks and is sensitive to the dropout operation. However, in this paper, w...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	272,880
2304.04681	Controllable Motion Synthesis and Reconstruction with Autoregressive Diffusion Models	Data-driven and controllable human motion synthesis and prediction are active research areas with various applications in interactive media and social robotics. Challenges remain in these fields for generating diverse motions given past observations and dealing with imperfect poses. This paper introduces MoDiff, an aut...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	357,313
2303.13300	The Innovation Paradox: Concept Space Expansion with Diminishing Originality and the Promise of Creative AI	Innovation, typically spurred by reusing, recombining, and synthesizing existing concepts, is expected to result in an exponential growth of the concept space over time. However, our statistical analysis of TechNet, which is a comprehensive technology semantic network encompassing over four million concepts derived fro...	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	false	353,615
2202.06836	A Machine Learning Framework for Event Identification via Modal Analysis of PMU Data	Power systems are prone to a variety of events (e.g. line trips and generation loss) and real-time identification of such events is crucial in terms of situational awareness, reliability, and security. Using measurements from multiple synchrophasors, i.e., phasor measurement units (PMUs), we propose to identify events ...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	280,350
2311.13615	HEViTPose: High-Efficiency Vision Transformer for Human Pose Estimation	Human pose estimation in complicated situations has always been a challenging task. Many Transformer-based pose networks have been proposed recently, achieving encouraging progress in improving performance. However, the remarkable performance of pose networks is always accompanied by heavy computation costs and large n...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	409,803
1711.06729	Phonological (un)certainty weights lexical activation	Spoken word recognition involves at least two basic computations. First is matching acoustic input to phonological categories (e.g. /b/, /p/, /d/). Second is activating words consistent with those phonological categories. Here we test the hypothesis that the listener's probability distribution over lexical items is wei...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	84,833
2410.03870	From Pixels to Personas: Investigating and Modeling Self-Anthropomorphism in Human-Robot Dialogues	Self-anthropomorphism in robots manifests itself through their display of human-like characteristics in dialogue, such as expressing preferences and emotions. Our study systematically analyzes self-anthropomorphic expression within various dialogue datasets, outlining the contrasts between self-anthropomorphic and non-...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	495,019
2003.09586	Probing Word Translations in the Transformer and Trading Decoder for Encoder Layers	Due to its effectiveness and performance, the Transformer translation model has attracted wide attention, most recently in terms of probing-based approaches. Previous work focuses on using or probing source linguistic features in the encoder. To date, the way word translation evolves in Transformer layers has not yet b...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	169,091
2010.03997	Unconstrained Text Detection in Manga	The detection and recognition of unconstrained text is an open problem in research. Text in comic books has unusual styles that raise many challenges for text detection. This work aims to identify text characters at a pixel level in a comic genre with highly sophisticated text styles: Japanese manga. To overcome the la...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	199,584
1704.04163	Spectrum Approximation Beyond Fast Matrix Multiplication: Algorithms and Hardness	Understanding the singular value spectrum of a matrix $A \in \mathbb{R}^{n \times n}$ is a fundamental task in countless applications. In matrix multiplication time, it is possible to perform a full SVD and directly compute the singular values $\sigma_1,...,\sigma_n$. However, little is known about algorithms that brea...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	71,762
2310.05952	Mitigating Denial of Service Attacks in Fog-Based Wireless Sensor Networks Using Machine Learning Techniques	Wireless sensor networks are considered to be among the most significant and innovative technologies in the 21st century due to their wide range of industrial applications. Sensor nodes in these networks are susceptible to a variety of assaults due to their special qualities and method of deployment. In WSNs, denial of...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	398,369
1602.02990	Self-organized control for musculoskeletal robots	With the accelerated development of robot technologies, optimal control becomes one of the central themes of research. In traditional approaches, the controller, by its internal functionality, finds appropriate actions on the basis of the history of sensor values, guided by the goals, intentions, objectives, learning s...	false	false	false	false	false	false	true	true	false	false	true	false	false	false	false	false	false	false	51,940
2307.03311	On Invariance, Equivariance, Correlation and Convolution of Spherical Harmonic Representations for Scalar and Vectorial Data	The mathematical representations of data in the Spherical Harmonic (SH) domain has recently regained increasing interest in the machine learning community. This technical report gives an in-depth introduction to the theoretical foundation and practical implementation of SH representations, summarizing works on rotation...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	377,987
2210.01425	Unveiling the Black Box of PLMs with Semantic Anchors: Towards Interpretable Neural Semantic Parsing	The recent prevalence of pretrained language models (PLMs) has dramatically shifted the paradigm of semantic parsing, where the mapping from natural language utterances to structured logical forms is now formulated as a Seq2Seq task. Despite the promising performance, previous PLM-based approaches often suffer from hal...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	321,259
1002.3192	Study of Gaussian Relay Channels with Correlated Noises	In this paper, we consider full-duplex and half-duplex Gaussian relay channels where the noises at the relay and destination are arbitrarily correlated. We first derive the capacity upper bound and the achievable rates with three existing schemes: Decode-and-Forward (DF), Compress-and-Forward (CF), and Amplify-and-Forw...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	5,720
2107.13686	AutoTinyBERT: Automatic Hyper-parameter Optimization for Efficient Pre-trained Language Models	Pre-trained language models (PLMs) have achieved great success in natural language processing. Most of PLMs follow the default setting of architecture hyper-parameters (e.g., the hidden dimension is a quarter of the intermediate dimension in feed-forward sub-networks) in BERT (Devlin et al., 2019). Few studies have bee...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	248,271
1706.07881	On Sampling Strategies for Neural Network-based Collaborative Filtering	Recent advances in neural networks have inspired people to design hybrid recommendation algorithms that can incorporate both (1) user-item interaction information and (2) content information including image, audio, and text. Despite their promising results, neural network-based recommendation algorithms pose extensive ...	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	false	75,909
1910.04358	Fast Processing and Querying of 170TB of Genomics Data via a Repeated And Merged BloOm Filter (RAMBO)	DNA sequencing, especially of microbial genomes and metagenomes, has been at the core of recent research advances in large-scale comparative genomics. The data deluge has resulted in exponential growth in genomic datasets over the past years and has shown no sign of slowing down. Several recent attempts have been made ...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	148,745
2203.11355	Origami in N dimensions: How feed-forward networks manufacture linear separability	Neural networks can implement arbitrary functions. But, mechanistically, what are the tools at their disposal to construct the target? For classification tasks, the network must transform the data classes into a linearly separable representation in the final hidden layer. We show that a feed-forward architecture has on...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	286,876
1605.04557	Social contagions on time-varying community networks	Time-varying community structures widely exist in various real-world networks. However, the spreading dynamics on this kind of network has not been fully studied. To this end, we systematically study the effects of time-varying community structures on social contagions. We first propose a non-Markovian social contagion...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	55,881
2212.12301	NoSQL Database Tuning through Machine Learning	NoSQL databases have become an important component of many big data and real-time web applications. Their distributed nature and scalability make them an ideal data storage repository for a variety of use cases. While NoSQL databases are delivered with a default ''off-the-shelf'' configuration, they offer configuration...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	338,008
2407.10795	Multilingual Contrastive Decoding via Language-Agnostic Layers Skipping	Decoding by contrasting layers (DoLa), is designed to improve the generation quality of large language models (LLMs) by contrasting the prediction probabilities between an early exit output (amateur logits) and the final output (expert logits). However, we find that this approach does not work well on non-English tasks...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	473,130
2409.14955	Efficient Collision Detection Framework for Enhancing Collision-Free Robot Motion	Fast and efficient collision detection is essential for motion generation in robotics. In this paper, we propose an efficient collision detection framework based on the Signed Distance Field (SDF) of robots, seamlessly integrated with a self-collision detection module. Firstly, we decompose the robot's SDF using forwar...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	490,700
2305.09211	CB-HVTNet: A channel-boosted hybrid vision transformer network for lymphocyte assessment in histopathological images	Transformers, due to their ability to learn long range dependencies, have overcome the shortcomings of convolutional neural networks (CNNs) for global perspective learning. Therefore, they have gained the focus of researchers for several vision related tasks including medical diagnosis. However, their multi-head attent...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	364,561
2408.16798	Generative AI in Ship Design	The process of ship design is intricate, heavily influenced by the hull form which accounts for approximately 70% of the total cost. Traditional methods rely on human-driven iterative processes based on naval architecture principles and engineering analysis. In contrast, generative AI presents a novel approach, utilizi...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	484,456
2204.07344	CAiD: Context-Aware Instance Discrimination for Self-supervised Learning in Medical Imaging	Recently, self-supervised instance discrimination methods have achieved significant success in learning visual representations from unlabeled photographic images. However, given the marked differences between photographic and medical images, the efficacy of instance-based objectives, focusing on learning the most discr...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	291,663
1810.03758	A Summary of the 4th International Workshop on Recovering 6D Object Pose	This document summarizes the 4th International Workshop on Recovering 6D Object Pose which was organized in conjunction with ECCV 2018 in Munich. The workshop featured four invited talks, oral and poster presentations of accepted workshop papers, and an introduction of the BOP benchmark for 6D object pose estimation. T...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	109,877
2407.09048	KUNPENG: An Embodied Large Model for Intelligent Maritime	Intelligent maritime, as an essential component of smart ocean construction, deeply integrates advanced artificial intelligence technology and data analysis methods, which covers multiple aspects such as smart vessels, route optimization, safe navigation, aiming to enhance the efficiency of ocean resource utilization a...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	472,431
2303.13825	HandNeRF: Neural Radiance Fields for Animatable Interacting Hands	We propose a novel framework to reconstruct accurate appearance and geometry with neural radiance fields (NeRF) for interacting hands, enabling the rendering of photo-realistic images and videos for gesture animation from arbitrary views. Given multi-view images of a single hand or interacting hands, an off-the-shelf s...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	353,847
1612.00712	Probabilistic Neural Programs	We present probabilistic neural programs, a framework for program induction that permits flexible specification of both a computational model and inference algorithm while simultaneously enabling the use of deep neural networks. Probabilistic neural programs combine a computation graph for specifying a neural network w...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	true	false	false	64,936
2111.06590	Data-Driven Pole Placement in LMI Regions with Robustness Constraints	This paper proposes a robust learning methodology to place the closed-loop poles in desired convex regions in the complex plane. We considered the system state and input matrices to be unknown and can only use the measurements of the system trajectories. The closed-loop pole placement problem in the linear matrix inequ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	266,116
2001.01565	Stance Detection Benchmark: How Robust Is Your Stance Detection?	Stance Detection (StD) aims to detect an author's stance towards a certain topic or claim and has become a key component in applications like fake news detection, claim validation, and argument search. However, while stance is easily detected by humans, machine learning models are clearly falling short of this task. Gi...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	159,515
1711.03345	Frangi-Net: A Neural Network Approach to Vessel Segmentation	In this paper, we reformulate the conventional 2-D Frangi vesselness measure into a pre-weighted neural network ("Frangi-Net"), and illustrate that the Frangi-Net is equivalent to the original Frangi filter. Furthermore, we show that, as a neural network, Frangi-Net is trainable. We evaluate the proposed method on a se...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	84,194
2410.13863	Fluid: Scaling Autoregressive Text-to-image Generative Models with Continuous Tokens	Scaling up autoregressive models in vision has not proven as beneficial as in large language models. In this work, we investigate this scaling problem in the context of text-to-image generation, focusing on two critical factors: whether models use discrete or continuous tokens, and whether tokens are generated in a ran...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	499,738
1904.03328	Mitigating Gyral Bias in Cortical Tractography via Asymmetric Fiber Orientation Distributions	Diffusion tractography in brain connectomics often involves tracing axonal trajectories across gray-white matter boundaries in gyral blades of complex cortical convolutions. To date, gyral bias is observed in most tractography algorithms with streamlines predominantly terminating at gyral crowns instead of sulcal banks...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	126,681
1505.07206	Uplink Downlink Rate Balancing in Cooperating Cellular Networks	Broadcast MIMO techniques can significantly increase the throughput in the downlink of cellular networks, at the price of channel state information (CSI) feedback from the mobiles, sent over the uplink. Thus, it creates a mechanism that can tradeoff some uplink capacity for increased downlink capacity. In this work we ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	43,525
2206.01441	Exploring Transformers for Behavioural Biometrics: A Case Study in Gait Recognition	Biometrics on mobile devices has attracted a lot of attention in recent years as it is considered a user-friendly authentication method. This interest has also been motivated by the success of Deep Learning (DL). Architectures based on Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) have been ...	true	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	300,478
2009.10434	Frame-wise Cross-modal Matching for Video Moment Retrieval	Video moment retrieval targets at retrieving a moment in a video for a given language query. The challenges of this task include 1) the requirement of localizing the relevant moment in an untrimmed video, and 2) bridging the semantic gap between textual query and video contents. To tackle those problems, early approach...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	196,892
2108.13782	Robust Symbol-Level Precoding and Passive Beamforming for IRS-Aided Communications	This paper investigates a joint beamforming design in a multiuser multiple-input single-output (MISO) communication network aided with an intelligent reflecting surface (IRS) panel. The symbol-level precoding (SLP) is adopted to enhance the system performance by exploiting the multiuser interference (MUI) with consider...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	252,903
2204.00212	Effect and Analysis of Large-scale Language Model Rescoring on Competitive ASR Systems	Large-scale language models (LLMs) such as GPT-2, BERT and RoBERTa have been successfully applied to ASR N-best rescoring. However, whether or how they can benefit competitive, near state-of-the-art ASR systems remains unexplored. In this study, we incorporate LLM rescoring into one of the most competitive ASR baseline...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	289,183
1807.07853	Surgical Phase Recognition of Short Video Shots Based on Temporal Modeling of Deep Features	Recognizing the phases of a laparoscopic surgery (LS) operation form its video constitutes a fundamental step for efficient content representation, indexing and retrieval in surgical video databases. In the literature, most techniques focus on phase segmentation of the entire LS video using hand-crafted visual features...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	103,397
1709.00025	A State-Space Approach to Dynamic Nonnegative Matrix Factorization	Nonnegative matrix factorization (NMF) has been actively investigated and used in a wide range of problems in the past decade. A significant amount of attention has been given to develop NMF algorithms that are suitable to model time series with strong temporal dependencies. In this paper, we propose a novel state-spac...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	79,837
2201.11165	First-Order Context-Specific Likelihood Weighting in Hybrid Probabilistic Logic Programs	Statistical relational AI and probabilistic logic programming have so far mostly focused on discrete probabilistic models. The reasons for this is that one needs to provide constructs to succinctly model the independencies in such models, and also provide efficient inference. Three types of independencies are importa...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	277,202
2502.06643	MoETuner: Optimized Mixture of Expert Serving with Balanced Expert Placement and Token Routing	Mixture-of-Experts (MoE) model architecture has emerged as a promising solution for scaling transformer models efficiently, offering sparse activation that reduces computational costs while increasing model capacity. However, as MoE models scale, they need to be distributed across GPU devices, thus face critical perfor...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	532,164
2309.08589	Chain-of-Thought Reasoning is a Policy Improvement Operator	Large language models have astounded the world with fascinating new capabilities. However, they currently lack the ability to teach themselves new skills, relying instead on large amounts of human-generated training data. We introduce SECToR (Self-Education via Chain-of-Thought Reasoning), a proof-of-concept demonstrat...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	392,235
2311.02240	Towards Machine Unlearning Benchmarks: Forgetting the Personal Identities in Facial Recognition Systems	Machine unlearning is a crucial tool for enabling a classification model to forget specific data that are used in the training time. Recently, various studies have presented machine unlearning algorithms and evaluated their methods on several datasets. However, most of the current machine unlearning algorithms have bee...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	405,350
2205.08606	Multibit Tries Packet Classification with Deep Reinforcement Learning	High performance packet classification is a key component to support scalable network applications like firewalls, intrusion detection, and differentiated services. With ever increasing in the line-rate in core networks, it becomes a great challenge to design a scalable and high performance packet classification soluti...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	296,994
1009.3665	A Dynamic Data Middleware Cache for Rapidly-growing Scientific Repositories	Modern scientific repositories are growing rapidly in size. Scientists are increasingly interested in viewing the latest data as part of query results. Current scientific middleware cache systems, however, assume repositories are static. Thus, they cannot answer scientific queries with the latest data. The queries, ins...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	7,590
1904.04620	Gaussian YOLOv3: An Accurate and Fast Object Detector Using Localization Uncertainty for Autonomous Driving	The use of object detection algorithms is becoming increasingly important in autonomous vehicles, and object detection at high accuracy and a fast inference speed is essential for safe autonomous driving. A false positive (FP) from a false localization during autonomous driving can lead to fatal accidents and hinder sa...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	127,083
2107.10659	Differentially Private Algorithms for 2020 Census Detailed DHC Race \& Ethnicity	This article describes a proposed differentially private (DP) algorithms that the US Census Bureau is considering to release the Detailed Demographic and Housing Characteristics (DHC) Race & Ethnicity tabulations as part of the 2020 Census. The tabulations contain statistics (counts) of demographic and housing characte...	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	true	false	247,364
2502.13042	Network-Realized Model Predictive Control Part I: NRF-Enabled Closed-loop Decomposition	A two-layer control architecture is proposed, which promotes scalable implementations for model predictive controllers. The top layer acts as both reference governor for the bottom layer, and as a feedback controller for the regulated network. By employing set-based methods, global theoretical guarantees are obtained b...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	535,178
2410.09105	Artificial intelligence techniques in inherited retinal diseases: A review	Inherited retinal diseases (IRDs) are a diverse group of genetic disorders that lead to progressive vision loss and are a major cause of blindness in working-age adults. The complexity and heterogeneity of IRDs pose significant challenges in diagnosis, prognosis, and management. Recent advancements in artificial intell...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	497,437
1610.04256	Assessing Threat of Adversarial Examples on Deep Neural Networks	Deep neural networks are facing a potential security threat from adversarial examples, inputs that look normal but cause an incorrect classification by the deep neural network. For example, the proposed threat could result in hand-written digits on a scanned check being incorrectly classified but looking normal when hu...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	62,363
2312.01656	The Contemporary Art of Image Search: Iterative User Intent Expansion via Vision-Language Model	Image search is an essential and user-friendly method to explore vast galleries of digital images. However, existing image search methods heavily rely on proximity measurements like tag matching or image similarity, requiring precise user inputs for satisfactory results. To meet the growing demand for a contemporary im...	true	false	false	false	true	true	false	false	false	false	false	true	false	false	false	false	false	false	412,534
1903.00933	Detecting dementia in Mandarin Chinese using transfer learning from a parallel corpus	Machine learning has shown promise for automatic detection of Alzheimer's disease (AD) through speech; however, efforts are hampered by a scarcity of data, especially in languages other than English. We propose a method to learn a correspondence between independently engineered lexicosyntactic features in two languages...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	123,141
1511.03908	Learning Human Identity from Motion Patterns	We present a large-scale study exploring the capability of temporal deep neural networks to interpret natural human kinematics and introduce the first method for active biometric authentication with mobile inertial sensors. At Google, we have created a first-of-its-kind dataset of human movements, passively collected b...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	true	false	false	48,814
1905.07318	Stochastically Dominant Distributional Reinforcement Learning	We describe a new approach for managing aleatoric uncertainty in the Reinforcement Learning (RL) paradigm. Instead of selecting actions according to a single statistic, we propose a distributional method based on the second-order stochastic dominance (SSD) relation. This compares the inherent dispersion of random retur...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	131,197
1609.08787	Mitigating Pilot Contamination Through Location-Aware Pilot Assignment in Massive MIMO Networks	We propose a novel location-aware pilot assignment scheme to mitigate pilot contamination in massive multiple-input multiple-output (MIMO) networks, where the channels are subjected to Rician fading. Our proposed scheme utilizes the location information of users as the input to conduct pilot assignment in the network. ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	61,635
2102.05673	Real-Time Likelihood-Free Inference of Roman Binary Microlensing Events with Amortized Neural Posterior Estimation	Fast and automated inference of binary-lens, single-source (2L1S) microlensing events with sampling-based Bayesian algorithms (e.g., Markov Chain Monte Carlo; MCMC) is challenged on two fronts: high computational cost of likelihood evaluations with microlensing simulation codes, and a pathological parameter space where...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	219,508
2205.07519	Fair Shares: Feasibility, Domination and Incentives	We consider fair allocation of a set $M$ of indivisible goods to $n$ equally-entitled agents, with no monetary transfers. Every agent $i$ has a valuation $v_i$ from some given class of valuation functions. A share $s$ is a function that maps a pair $(v_i,n)$ to a value, with the interpretation that if an allocation of ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	296,630

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