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
2012.04382	Using Feature Alignment Can Improve Clean Average Precision and Adversarial Robustness in Object Detection	The 2D object detection in clean images has been a well studied topic, but its vulnerability against adversarial attack is still worrying. Existing work has improved robustness of object detectors by adversarial training, at the same time, the average precision (AP) on clean images drops significantly. In this paper, w...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	210,443
1205.3569	The Simulation and Mapping of Building Performance Indicators based on European Weather Stations	Due to the climate change debate, a lot of research and maps of external climate parameters are available. However, maps of indoor climate performance parameters are still lacking. This paper presents a methodology for obtaining maps of performances of similar buildings that are virtually spread over whole Europe. The ...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	16,033
1805.06597	ARUM: Polar Coded HARQ Scheme based on Incremental Channel Polarization	A hybrid ARQ (HARQ) scheme for polar code, which is called active-bit relocation under masks (ARUM), is proposed. In each transmission, the data bits are encoded and bit-wisely XOR-masked using a binary vector before being transmitted through the channel. The masking process combines multiple transmissions together whi...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	97,645
2011.13163	Being Central on the Cheap: Stability in Heterogeneous Multiagent Centrality Games	We study strategic network formation games in which agents attempt to form (costly) links in order to maximize their network centrality. Our model derives from Jackson and Wolinsky's symmetric connection model, but allows for heterogeneity in agent utilities by replacing decay centrality (implicit in the Jackson-Wolins...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	208,396
1811.03423	dAIrector: Automatic Story Beat Generation through Knowledge Synthesis	dAIrector is an automated director which collaborates with humans storytellers for live improvisational performances and writing assistance. dAIrector can be used to create short narrative arcs through contextual plot generation. In this work, we present the system architecture, a quantitative evaluation of design choi...	true	false	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	112,836
2301.09790	The Next Chapter: A Study of Large Language Models in Storytelling	To enhance the quality of generated stories, recent story generation models have been investigating the utilization of higher-level attributes like plots or commonsense knowledge. The application of prompt-based learning with large language models (LLMs), exemplified by GPT-3, has exhibited remarkable performance in di...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	341,605
2005.03627	Universal Coding and Prediction on Ergodic Martin-L\"of Random Points	Suppose that we have a method which estimates the conditional probabilities of some unknown stochastic source and we use it to guess which of the outcomes will happen. We want to make a correct guess as often as it is possible. What estimators are good for this? In this work, we consider estimators given by a familiar ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	176,211
2109.02237	BERT might be Overkill: A Tiny but Effective Biomedical Entity Linker based on Residual Convolutional Neural Networks	Biomedical entity linking is the task of linking entity mentions in a biomedical document to referent entities in a knowledge base. Recently, many BERT-based models have been introduced for the task. While these models have achieved competitive results on many datasets, they are computationally expensive and contain ab...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	253,679
2209.09422	Bit Allocation using Optimization	In this paper, we consider the problem of bit allocation in Neural Video Compression (NVC). First, we reveal a fundamental relationship between bit allocation in NVC and Semi-Amortized Variational Inference (SAVI). Specifically, we show that SAVI with GoP (Group-of-Picture)-level likelihood is equivalent to pixel-level...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	318,500
2410.03375	SoundSignature: What Type of Music Do You Like?	SoundSignature is a music application that integrates a custom OpenAI Assistant to analyze users' favorite songs. The system incorporates state-of-the-art Music Information Retrieval (MIR) Python packages to combine extracted acoustic/musical features with the assistant's extensive knowledge of the artists and bands. C...	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	494,749
2410.15802	Assisted Physical Interaction: Autonomous Aerial Robots with Neural Network Detection, Navigation, and Safety Layers	The paper introduces a novel framework for safe and autonomous aerial physical interaction in industrial settings. It comprises two main components: a neural network-based target detection system enhanced with edge computing for reduced onboard computational load, and a control barrier function (CBF)-based controller f...	false	false	false	false	false	false	false	true	false	false	true	true	false	false	false	false	false	false	500,734
2003.12697	Semantically Multi-modal Image Synthesis	In this paper, we focus on semantically multi-modal image synthesis (SMIS) task, namely, generating multi-modal images at the semantic level. Previous work seeks to use multiple class-specific generators, constraining its usage in datasets with a small number of classes. We instead propose a novel Group Decreasing Netw...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	169,983
2202.08486	Energy-Efficient UAV Communications: A Generalised Propulsion Energy Consumption Model	This paper proposes a generalised propulsion energy consumption model (PECM) for rotary-wing ummanned aerial vehicles (UAVs) under the consideration of the practical thrust-to-weight ratio (TWR) with respect to the velocity, acceleration and direction change of the UAVs. To verify the effectiveness of the proposed PECM...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	280,891
2501.04752	A mathematical model for the bullying dynamics in schools	We analyze a mathematical model to understand the dynamics of bullying in schools. The model considers a population divided into four groups: susceptible individuals, bullies, individuals exposed to bullying, and violent individuals. Transitions between these states occur at rates designed to capture the complex intera...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	523,330
2303.06277	SPOTR: Spatio-temporal Pose Transformers for Human Motion Prediction	3D human motion prediction is a research area of high significance and a challenge in computer vision. It is useful for the design of many applications including robotics and autonomous driving. Traditionally, autogregressive models have been used to predict human motion. However, these models have high computation nee...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	350,772
1712.01511	Successive Embedding and Classification Loss for Aerial Image Classification	Deep neural networks can be effective means to automatically classify aerial images but is easy to overfit to the training data. It is critical for trained neural networks to be robust to variations that exist between training and test environments. To address the overfitting problem in aerial image classification, we ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	86,116
2406.06432	SYM3D: Learning Symmetric Triplanes for Better 3D-Awareness of GANs	Despite the growing success of 3D-aware GANs, which can be trained on 2D images to generate high-quality 3D assets, they still rely on multi-view images with camera annotations to synthesize sufficient details from all viewing directions. However, the scarce availability of calibrated multi-view image datasets, especia...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	462,571
2101.11981	Embedding Symbolic Temporal Knowledge into Deep Sequential Models	Sequences and time-series often arise in robot tasks, e.g., in activity recognition and imitation learning. In recent years, deep neural networks (DNNs) have emerged as an effective data-driven methodology for processing sequences given sufficient training data and compute resources. However, when data is limited, simp...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	217,449
2202.04611	Task Modifiers for HTN Planning and Acting	The ability of an agent to change its objectives in response to unexpected events is desirable in dynamic environments. In order to provide this capability to hierarchical task network (HTN) planning, we propose an extension of the paradigm called task modifiers, which are functions that receive a task list and a state...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	279,611
2211.16676	Robust Learning of Nonlinear Dynamical Systems with Safety and Stability Properties	The paper presents a robust parameter learning methodology for identification of nonlinear dynamical system from data while satisfying safety and stability constraints in the context of learning from demonstration (LfD) methods. Extreme Learning Machines (ELM) is used to approximate the system model, whose parameters a...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	333,702
1705.08923	Attention-based Natural Language Person Retrieval	Following the recent progress in image classification and captioning using deep learning, we develop a novel natural language person retrieval system based on an attention mechanism. More specifically, given the description of a person, the goal is to localize the person in an image. To this end, we first construct a b...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	74,107
2006.05138	Sparse Dynamic Distribution Decomposition: Efficient Integration of Trajectory and Snapshot Time Series Data	Dynamic Distribution Decomposition (DDD) was introduced in Taylor-King et. al. (PLOS Comp Biol, 2020) as a variation on Dynamic Mode Decomposition. In brief, by using basis functions over a continuous state space, DDD allows for the fitting of continuous-time Markov chains over these basis functions and as a result con...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	180,953
2202.08930	A Distributed Algorithm for Measure-valued Optimization with Additive Objective	We propose a distributed nonparametric algorithm for solving measure-valued optimization problems with additive objectives. Such problems arise in several contexts in stochastic learning and control including Langevin sampling from an unnormalized prior, mean field neural network learning and Wasserstein gradient flows...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	281,028
1612.06271	Distributed Optimization of Hierarchical Small Cell Networks: A GNEP Framework	Deployment of small cell base stations (SBSs) overlaying the coverage area of a macrocell BS (MBS) results in a two-tier hierarchical small cell network. Cross-tier and inter-tier interference not only jeopardize primary macrocell communication but also limit the spectral efficiency of small cell communication. This pa...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	65,798
2209.04779	Scattering Model Guided Adversarial Examples for SAR Target Recognition: Attack and Defense	Deep Neural Networks (DNNs) based Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) systems have shown to be highly vulnerable to adversarial perturbations that are deliberately designed yet almost imperceptible but can bias DNN inference when added to targeted objects. This leads to serious safety conc...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	316,880
2404.18534	Evaluating and Mitigating Linguistic Discrimination in Large Language Models	By training on text in various languages, large language models (LLMs) typically possess multilingual support and demonstrate remarkable capabilities in solving tasks described in different languages. However, LLMs can exhibit linguistic discrimination due to the uneven distribution of training data across languages. T...	false	false	false	false	true	false	false	false	true	false	false	false	true	false	false	false	false	true	450,299
2109.10078	Learning Interpretable Concept Groups in CNNs	We propose a novel training methodology -- Concept Group Learning (CGL) -- that encourages training of interpretable CNN filters by partitioning filters in each layer into concept groups, each of which is trained to learn a single visual concept. We achieve this through a novel regularization strategy that forces filte...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	256,503
2007.10137	On Coresets for Fair Clustering in Metric and Euclidean Spaces and Their Applications	Fair clustering is a constrained variant of clustering where the goal is to partition a set of colored points, such that the fraction of points of any color in every cluster is more or less equal to the fraction of points of this color in the dataset. This variant was recently introduced by Chierichetti et al. [NeurIPS...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	188,186
1906.06626	REMAP: Multi-layer entropy-guided pooling of dense CNN features for image retrieval	This paper addresses the problem of very large-scale image retrieval, focusing on improving its accuracy and robustness. We target enhanced robustness of search to factors such as variations in illumination, object appearance and scale, partial occlusions, and cluttered backgrounds - particularly important when search ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	135,364
2208.11294	Collaborative Remote Control of Unmanned Ground Vehicles in Virtual Reality	Virtual reality (VR) technology is commonly used in entertainment applications; however, it has also been deployed in practical applications in more serious aspects of our lives, such as safety. To support people working in dangerous industries, VR can ensure operators manipulate standardized tasks and work collaborati...	true	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	314,372
2401.02952	Optimizing Dataflow Systems for Scalable Interactive Visualization	Supporting the interactive exploration of large datasets is a popular and challenging use case for data management systems. Traditionally, the interface and the back-end system are built and optimized separately, and interface design and system optimization require different skill sets that are difficult for one person...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	419,900
2302.04500	FLAC: A Robust Failure-Aware Atomic Commit Protocol for Distributed Transactions	In distributed transaction processing, atomic commit protocol (ACP) is used to ensure database consistency. With the use of commodity compute nodes and networks, failures such as system crashes and network partitioning are common. It is therefore important for ACP to dynamically adapt to the operating condition for eff...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	true	true	344,730
2411.17993	DRS: Deep Question Reformulation With Structured Output	Question answering represents a core capability of large language models (LLMs). However, when individuals encounter unfamiliar knowledge in texts, they often formulate questions that the text itself cannot answer due to insufficient understanding of the underlying information. Recent studies reveal that while LLMs can...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	511,684
1511.02506	Towards Structured Deep Neural Network for Automatic Speech Recognition	In this paper we propose the Structured Deep Neural Network (structured DNN) as a structured and deep learning framework. This approach can learn to find the best structured object (such as a label sequence) given a structured input (such as a vector sequence) by globally considering the mapping relationships between t...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	true	false	false	48,646
1803.02933	Distributed Computation of Wasserstein Barycenters over Networks	We propose a new \cu{class-optimal} algorithm for the distributed computation of Wasserstein Barycenters over networks. Assuming that each node in a graph has a probability distribution, we prove that every node can reach the barycenter of all distributions held in the network by using local interactions compliant with...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	true	92,158
2407.12599	On Diversity in Discriminative Neural Networks	Diversity is a concept of prime importance in almost all disciplines based on information processing. In telecommunications, for example, spatial, temporal, and frequency diversity, as well as redundant coding, are fundamental concepts that have enabled the design of extremely efficient systems. In machine learning, in...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	473,995
2412.16635	Task-Driven Co-Design of Mobile Manipulators	Recent interest in mobile manipulation has resulted in a wide range of new robot designs. A large family of these designs focuses on modular platforms that combine existing mobile bases with static manipulator arms. They combine these modules by mounting the arm in a tabletop configuration. However, the operating works...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	519,616
2401.03862	End-to-End Crystal Structure Prediction from Powder X-Ray Diffraction	Powder X-ray diffraction (PXRD) is a prevalent technique in materials characterization. While the analysis of PXRD often requires extensive human manual intervention, and most automated method only achieved at coarse-grained level. The more difficult and important task of fine-grained crystal structure prediction from ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	420,251
2408.09371	Detecting the Undetectable: Combining Kolmogorov-Arnold Networks and MLP for AI-Generated Image Detection	As artificial intelligence progresses, the task of distinguishing between real and AI-generated images is increasingly complicated by sophisticated generative models. This paper presents a novel detection framework adept at robustly identifying images produced by cutting-edge generative AI models, such as DALL-E 3, Mid...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	481,404
2203.07454	L2Explorer: A Lifelong Reinforcement Learning Assessment Environment	Despite groundbreaking progress in reinforcement learning for robotics, gameplay, and other complex domains, major challenges remain in applying reinforcement learning to the evolving, open-world problems often found in critical application spaces. Reinforcement learning solutions tend to generalize poorly when exposed...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	285,429
1909.13168	Optimizing Design Verification using Machine Learning: Doing better than Random	As integrated circuits have become progressively more complex, constrained random stimulus has become ubiquitous as a means of stimulating a designs functionality and ensuring it fully meets expectations. In theory, random stimulus allows all possible combinations to be exercised given enough time, but in practice with...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	147,351
2303.17564	BloombergGPT: A Large Language Model for Finance	The use of NLP in the realm of financial technology is broad and complex, with applications ranging from sentiment analysis and named entity recognition to question answering. Large Language Models (LLMs) have been shown to be effective on a variety of tasks; however, no LLM specialized for the financial domain has bee...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	355,250
2303.12785	Matryoshka Policy Gradient for Entropy-Regularized RL: Convergence and Global Optimality	A novel Policy Gradient (PG) algorithm, called $\textit{Matryoshka Policy Gradient}$ (MPG), is introduced and studied, in the context of fixed-horizon max-entropy reinforcement learning, where an agent aims at maximizing entropy bonuses additional to its cumulative rewards. In the linear function approximation setting ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	353,398
2006.12376	A Convergent and Dimension-Independent Min-Max Optimization Algorithm	We study a variant of a recently introduced min-max optimization framework where the max-player is constrained to update its parameters in a greedy manner until it reaches a first-order stationary point. Our equilibrium definition for this framework depends on a proposal distribution which the min-player uses to choose...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	183,563
2009.11936	Observer-based Event-triggered Boundary Control of a Class of Reaction-Diffusion PDEs	This paper presents an observer-based event-triggered boundary control strategy for a class of reaction-diffusion PDEs with Robin actuation. The observer only requires boundary measurements. The control approach consists of a backstepping output feedback boundary controller, derived using estimated states, and a dynami...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	197,282
2004.03373	Improving BPSO-based feature selection applied to offline WI handwritten signature verification through overfitting control	This paper investigates the presence of overfitting when using Binary Particle Swarm Optimization (BPSO) to perform the feature selection in a context of Handwritten Signature Verification (HSV). SigNet is a state of the art Deep CNN model for feature representation in the HSV context and contains 2048 dimensions. Some...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	171,537
1810.03654	Joint Unsupervised Learning of Optical Flow and Depth by Watching Stereo Videos	Learning depth and optical flow via deep neural networks by watching videos has made significant progress recently. In this paper, we jointly solve the two tasks by exploiting the underlying geometric rules within stereo videos. Specifically, given two consecutive stereo image pairs from a video, we first estimate dept...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	109,854
2310.14868	Assessing Step-by-Step Reasoning against Lexical Negation: A Case Study on Syllogism	Large language models (LLMs) take advantage of step-by-step reasoning instructions, e.g., chain-of-thought (CoT) prompting. Building on this, their ability to perform CoT-style reasoning robustly is of interest from a probing perspective. In this study, we inspect the step-by-step reasoning ability of LLMs with a focus...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	402,057
2107.02704	Unsupervised learning of MRI tissue properties using MRI physics models	In neuroimaging, MRI tissue properties characterize underlying neurobiology, provide quantitative biomarkers for neurological disease detection and analysis, and can be used to synthesize arbitrary MRI contrasts. Estimating tissue properties from a single scan session using a protocol available on all clinical scanners...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	244,909
1507.06737	The Degrees of Freedom of the Interference Channel with a Cognitive Relay under Delayed Feedback	This paper studies the interference channel with a cognitive relay (ICCR) under delayed feedback. Three types of delayed feedback are studied: delayed channel state information at the transmitter (CSIT), delayed output feedback, and delayed Shannon feedback. Outer bounds are derived for the DoF region of the two-user m...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	45,410
2409.14744	LINKAGE: Listwise Ranking among Varied-Quality References for Non-Factoid QA Evaluation via LLMs	Non-Factoid (NF) Question Answering (QA) is challenging to evaluate due to diverse potential answers and no objective criterion. The commonly used automatic evaluation metrics like ROUGE or BERTScore cannot accurately measure semantic similarities or answers from different perspectives. Recently, Large Language Models ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	490,612
1406.4928	Diversity Multiplexing Tradeoff of the Half-duplex Slow Fading Multiple Access Channel based on Generalized Quantize-and-Forward Scheme	This paper investigates the Diversity Multiplexing Tradeoff (DMT) of the generalized quantize-and-forward (GQF) relaying scheme over the slow fading half-duplex multiple-access relay channel (HD-MARC). The compress-and-forward (CF) scheme has been shown to achieve the optimal DMT when the channel state information (CSI...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	33,987
2308.10334	Coordinate Transformer: Achieving Single-stage Multi-person Mesh Recovery from Videos	Multi-person 3D mesh recovery from videos is a critical first step towards automatic perception of group behavior in virtual reality, physical therapy and beyond. However, existing approaches rely on multi-stage paradigms, where the person detection and tracking stages are performed in a multi-person setting, while tem...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	386,683
2301.05664	Risk Sensitive Dead-end Identification in Safety-Critical Offline Reinforcement Learning	In safety-critical decision-making scenarios being able to identify worst-case outcomes, or dead-ends is crucial in order to develop safe and reliable policies in practice. These situations are typically rife with uncertainty due to unknown or stochastic characteristics of the environment as well as limited offline tra...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	340,417
2005.02811	Hierarchical Bayesian Approach for Improving Weights for Solving Multi-Objective Route Optimization Problem	The weighted sum method is a simple and widely used technique that scalarizes multiple conflicting objectives into a single objective function. It suffers from the problem of determining the appropriate weights corresponding to the objectives. This paper proposes a novel Hierarchical Bayesian model based on Multinomial...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	false	175,979
1710.02380	Active Attack on User Load Achieving Pilot Design in Massive MIMO Networks	In this paper, we propose an active attacking strategy on a massive multiple-input multiple-output (MIMO) network, where the pilot sequences are obtained using the user load-achieving pilot sequence design. The user load-achieving design ensures that the signal-to-interference-plus-noise ratio (SINR) requirements of al...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	82,166
2406.00011	DisCo: Towards Harmonious Disentanglement and Collaboration between Tabular and Semantic Space for Recommendation	Recommender systems play important roles in various applications such as e-commerce, social media, etc. Conventional recommendation methods usually model the collaborative signals within the tabular representation space. Despite the personalization modeling and the efficiency, the latent semantic dependencies are omitt...	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	459,649
2204.10846	Self-Supervised Video Object Segmentation via Cutout Prediction and Tagging	We propose a novel self-supervised Video Object Segmentation (VOS) approach that strives to achieve better object-background discriminability for accurate object segmentation. Distinct from previous self-supervised VOS methods, our approach is based on a discriminative learning loss formulation that takes into account ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	292,937
2305.07678	Exploring the Rate-Distortion-Complexity Optimization in Neural Image Compression	Despite a short history, neural image codecs have been shown to surpass classical image codecs in terms of rate-distortion performance. However, most of them suffer from significantly longer decoding times, which hinders the practical applications of neural image codecs. This issue is especially pronounced when employi...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	363,989
2201.09651	Artefact Retrieval: Overview of NLP Models with Knowledge Base Access	Many NLP models gain performance by having access to a knowledge base. A lot of research has been devoted to devising and improving the way the knowledge base is accessed and incorporated into the model, resulting in a number of mechanisms and pipelines. Despite the diversity of proposed mechanisms, there are patterns ...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	276,742
1704.04100	Cross-lingual and cross-domain discourse segmentation of entire documents	Discourse segmentation is a crucial step in building end-to-end discourse parsers. However, discourse segmenters only exist for a few languages and domains. Typically they only detect intra-sentential segment boundaries, assuming gold standard sentence and token segmentation, and relying on high-quality syntactic parse...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	71,749
1910.05312	Map Matching Algorithm for Large-scale Datasets	GPS receivers embedded in cell phones and connected vehicles generate a series of location measurements that can be used for various analytical purposes. A common pre-processing step of this data is the so-called map matching. The goal of map matching is to infer the trajectory that the device followed in a road networ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	149,022
2009.07724	SelfAugment: Automatic Augmentation Policies for Self-Supervised Learning	A common practice in unsupervised representation learning is to use labeled data to evaluate the quality of the learned representations. This supervised evaluation is then used to guide critical aspects of the training process such as selecting the data augmentation policy. However, guiding an unsupervised training pro...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	196,027
2207.01920	Social Sensing and Human in the Loop Profiling during Pandemics: the Vitoria application	As the number of smart devices that surround us increases, so do the opportunities to leverage them to create socially- and context-aware systems. Smart devices can be used for better understanding human behaviour and its societal implications. As an example of a scenario in which the role of socially aware systems is ...	true	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	306,346
2302.14490	Estimating Head Motion from MR-Images	Head motion is an omnipresent confounder of magnetic resonance image (MRI) analyses as it systematically affects morphometric measurements, even when visual quality control is performed. In order to estimate subtle head motion, that remains undetected by experts, we introduce a deep learning method to predict in-scanne...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	348,323
2008.02481	Machine Learning Based Framework for Estimation of Data Center Power Using Acoustic Side Channel	Data centers are high power consumers and the energy consumption of data centers keeps on rising in spite of all the efforts for increasing the energy efficiency. The need for energy-awareness in data centers makes the use of power modeling and estimation to be still a big challenge due to huge amount of uncertainty in...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	190,630
1109.5894	Learning Item Trees for Probabilistic Modelling of Implicit Feedback	User preferences for items can be inferred from either explicit feedback, such as item ratings, or implicit feedback, such as rental histories. Research in collaborative filtering has concentrated on explicit feedback, resulting in the development of accurate and scalable models. However, since explicit feedback is oft...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	12,353
1206.6859	Propagation of Delays in the National Airspace System	The National Airspace System (NAS) is a large and complex system with thousands of interrelated components: administration, control centers, airports, airlines, aircraft, passengers, etc. The complexity of the NAS creates many difficulties in management and control. One of the most pressing problems is flight delay. De...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	17,083
2002.09820	Deep Reinforcement Learning with Linear Quadratic Regulator Regions	Practitioners often rely on compute-intensive domain randomization to ensure reinforcement learning policies trained in simulation can robustly transfer to the real world. Due to unmodeled nonlinearities in the real system, however, even such simulated policies can still fail to perform stably enough to acquire experie...	false	false	false	false	true	false	true	true	false	false	true	false	false	false	false	false	false	false	165,194
1806.02389	Not All Attributes are Created Equal: $d_{\mathcal{X}}$-Private Mechanisms for Linear Queries	Differential privacy provides strong privacy guarantees simultaneously enabling useful insights from sensitive datasets. However, it provides the same level of protection for all elements (individuals and attributes) in the data. There are practical scenarios where some data attributes need more/less protection than ot...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	99,768
1212.5765	Stochastic Subspace Identification: Valid Model, Asymptotics and Model Error Bounds	This paper investigates the ability of the stochastic subspace identification technique to return a valid model from finite measurement data, its asymptotic properties as the data set becomes large, and asymptotic error bounds of the identified model (in terms of $\mathcal{H}_2$ and $\mathcal{H}_{\infty}$ norms). First...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	20,586
2404.15840	Constructive Interpolation and Concept-Based Beth Definability for Description Logics via Sequents	We introduce a constructive method applicable to a large number of description logics (DLs) for establishing the concept-based Beth definability property (CBP) based on sequent systems. Using the highly expressive DL RIQ as a case study, we introduce novel sequent calculi for RIQ-ontologies and show how certain interpo...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	true	true	449,262
2409.13291	Localized Gaussians as Self-Attention Weights for Point Clouds Correspondence	Current data-driven methodologies for point cloud matching demand extensive training time and computational resources, presenting significant challenges for model deployment and application. In the point cloud matching task, recent advancements with an encoder-only Transformer architecture have revealed the emergence o...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	489,931
2001.11402	Graph Convolution Machine for Context-aware Recommender System	The latest advance in recommendation shows that better user and item representations can be learned via performing graph convolutions on the user-item interaction graph. However, such finding is mostly restricted to the collaborative filtering (CF) scenario, where the interaction contexts are not available. In this wor...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	162,065
2205.09674	Disentangling Active and Passive Cosponsorship in the U.S. Congress	In the U.S. Congress, legislators can use active and passive cosponsorship to support bills. We show that these two types of cosponsorship are driven by two different motivations: the backing of political colleagues and the backing of the bill's content. To this end, we develop an Encoder+RGCN based model that learns l...	false	false	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	297,371
2404.15660	KS-LLM: Knowledge Selection of Large Language Models with Evidence Document for Question Answering	Large language models (LLMs) suffer from the hallucination problem and face significant challenges when applied to knowledge-intensive tasks. A promising approach is to leverage evidence documents as extra supporting knowledge, which can be obtained through retrieval or generation. However, existing methods directly le...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	449,184
2009.05214	Adversarial Learning for Zero-shot Domain Adaptation	Zero-shot domain adaptation (ZSDA) is a category of domain adaptation problems where neither data sample nor label is available for parameter learning in the target domain. With the hypothesis that the shift between a given pair of domains is shared across tasks, we propose a new method for ZSDA by transferring domain ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	195,257
2206.02422	Ego Network Structure in Online Social Networks and its Impact on Information Diffusion	In the last few years, Online Social Networks (OSNs) attracted the interest of a large number of researchers, thanks to their central role in the society. Through the analysis of OSNs, many social phenomena have been studied, such as the viral diffusion of information amongst people. What is still unclear is the relati...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	300,890
1603.06910	Degrees of Freedom of the Two-User MIMO Broadcast Channel with Private and Common Messages Under Hybrid CSIT Models	We study the degrees of freedom (DoF) regions of the two-user multiple-input multiple-output (MIMO) broadcast channel with a general message set (BC-CM) - that includes private and common messages - under fast fading. Nine different channel state knowledge assumptions -collectively known as hybrid CSIT models - are con...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	53,564
1907.12399	Lotka-Volterra competition mechanism embedded in a decision-making method	Decision making is a fundamental capability of living organisms, and has recently been gaining increasing importance in many engineering applications. Here, we consider a simple decision-making principle to identify an optimal choice in multi-armed bandit (MAB) problems, which is fundamental in the context of reinforce...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	140,114
2410.12011	Pixology: Probing the Linguistic and Visual Capabilities of Pixel-based Language Models	Pixel-based language models have emerged as a compelling alternative to subword-based language modelling, particularly because they can represent virtually any script. PIXEL, a canonical example of such a model, is a vision transformer that has been pre-trained on rendered text. While PIXEL has shown promising cross-sc...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	498,810
1503.00164	Analysis of Crowdsourced Sampling Strategies for HodgeRank with Sparse Random Graphs	Crowdsourcing platforms are now extensively used for conducting subjective pairwise comparison studies. In this setting, a pairwise comparison dataset is typically gathered via random sampling, either \emph{with} or \emph{without} replacement. In this paper, we use tools from random graph theory to analyze these two ra...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	40,670
2107.12342	Sisyphus: A Cautionary Tale of Using Low-Degree Polynomial Activations in Privacy-Preserving Deep Learning	Privacy concerns in client-server machine learning have given rise to private inference (PI), where neural inference occurs directly on encrypted inputs. PI protects clients' personal data and the server's intellectual property. A common practice in PI is to use garbled circuits to compute nonlinear functions privately...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	247,868
2409.17502	Broadcast Product: Shape-aligned Element-wise Multiplication and Beyond	We propose a new operator defined between two tensors, the broadcast product. The broadcast product calculates the Hadamard product after duplicating elements to align the shapes of the two tensors. Complex tensor operations in libraries like \texttt{numpy} can be succinctly represented as mathematical expressions usin...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	491,833
2208.12392	DiVa: An Accelerator for Differentially Private Machine Learning	The widespread deployment of machine learning (ML) is raising serious concerns on protecting the privacy of users who contributed to the collection of training data. Differential privacy (DP) is rapidly gaining momentum in the industry as a practical standard for privacy protection. Despite DP's importance, however, li...	false	false	false	false	true	false	true	false	false	false	false	false	true	false	false	false	false	true	314,708
1603.00400	A Fast Randomized Algorithm for Multi-Objective Query Optimization	Query plans are compared according to multiple cost metrics in multi-objective query optimization. The goal is to find the set of Pareto plans realizing optimal cost tradeoffs for a given query. So far, only algorithms with exponential complexity in the number of query tables have been proposed for multi-objective quer...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	52,770
1912.12747	Worst-Case Optimal Radix Triejoin	Relatively recently, the field of join processing has been swayed by the discovery of a new class of multi-way join algorithms. The new algorithms join multiple relations simultaneously rather than perform a series of pairwise joins. The new join algorithms satisfy stronger worst-case runtime complexity guarantees than...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	158,921
cs/0701006	The Trapping Redundancy of Linear Block Codes	We generalize the notion of the stopping redundancy in order to study the smallest size of a trapping set in Tanner graphs of linear block codes. In this context, we introduce the notion of the trapping redundancy of a code, which quantifies the relationship between the number of redundant rows in any parity-check matr...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	540,005
2212.11792	CatlNet: Learning Communication and Coordination Policies from CaTL+ Specifications	In this paper, we propose a learning-based framework to simultaneously learn the communication and distributed control policies for a heterogeneous multi-agent system (MAS) under complex mission requirements from Capability Temporal Logic plus (CaTL+) specifications. Both policies are trained, implemented, and deployed...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	337,891
1910.07563	Explainable AI for Intelligence Augmentation in Multi-Domain Operations	Central to the concept of multi-domain operations (MDO) is the utilization of an intelligence, surveillance, and reconnaissance (ISR) network consisting of overlapping systems of remote and autonomous sensors, and human intelligence, distributed among multiple partners. Realising this concept requires advancement in bo...	true	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	149,648
1008.2750	On BICM receivers for TCM transmission	Recent results have shown that the performance of bit-interleaved coded modulation (BICM) using convolutional codes in nonfading channels can be significantly improved when the interleaver takes a trivial form (BICM-T), i.e., when it does not interleave the bits at all. In this paper, we give a formal explanation for t...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	7,289
2112.03159	UniLog: Deploy One Model and Specialize it for All Log Analysis Tasks	UniLog: Deploy One Model and Specialize it for All Log Analysis Tasks	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	270,103
2403.15839	TablePuppet: A Generic Framework for Relational Federated Learning	Current federated learning (FL) approaches view decentralized training data as a single table, divided among participants either horizontally (by rows) or vertically (by columns). However, these approaches are inadequate for handling distributed relational tables across databases. This scenario requires intricate SQL o...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	true	440,770
0906.2511	Robust Rate-Adaptive Wireless Communication Using ACK/NAK-Feedback	To combat the detrimental effects of the variability in wireless channels, we consider cross-layer rate adaptation based on limited feedback. In particular, based on limited feedback in the form of link-layer acknowledgements (ACK) and negative acknowledgements (NAK), we maximize the physical-layer transmission rate su...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	3,875
2412.17957	ArchComplete: Autoregressive 3D Architectural Design Generation with Hierarchical Diffusion-Based Upsampling	Recent advances in 3D generative models have shown promising results but often fall short in capturing the complexity of architectural geometries and topologies and fine geometric details at high resolutions. To tackle this, we present ArchComplete, a two-stage voxel-based 3D generative pipeline consisting of a vector-...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	true	520,185
1806.00775	Exploration in Structured Reinforcement Learning	We address reinforcement learning problems with finite state and action spaces where the underlying MDP has some known structure that could be potentially exploited to minimize the exploration rates of suboptimal (state, action) pairs. For any arbitrary structure, we derive problem-specific regret lower bounds satisfie...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	99,392
2304.11961	Towards Mode Balancing of Generative Models via Diversity Weights	Large data-driven image models are extensively used to support creative and artistic work. Under the currently predominant distribution-fitting paradigm, a dataset is treated as ground truth to be approximated as closely as possible. Yet, many creative applications demand a diverse range of output, and creators often s...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	360,037
2410.11179	Interpretability as Compression: Reconsidering SAE Explanations of Neural Activations with MDL-SAEs	Sparse Autoencoders (SAEs) have emerged as a useful tool for interpreting the internal representations of neural networks. However, naively optimising SAEs for reconstruction loss and sparsity results in a preference for SAEs that are extremely wide and sparse. We present an information-theoretic framework for interpre...	false	false	false	false	true	false	true	false	false	true	false	false	false	false	false	false	false	false	498,421
2303.15349	Information Maximizing Curriculum: A Curriculum-Based Approach for Imitating Diverse Skills	Imitation learning uses data for training policies to solve complex tasks. However, when the training data is collected from human demonstrators, it often leads to multimodal distributions because of the variability in human actions. Most imitation learning methods rely on a maximum likelihood (ML) objective to learn a...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	354,456
1607.00421	From Migration Corridors to Clusters: The Value of Google+ Data for Migration Studies	Recently, there have been considerable efforts to use online data to investigate international migration. These efforts show that Web data are valuable for estimating migration rates and are relatively easy to obtain. However, existing studies have only investigated flows of people along migration corridors, i.e. betwe...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	58,069

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