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
2410.22067	Backstepping Control of Continua of Linear Hyperbolic PDEs and Application to Stabilization of Large-Scale $n+m$ Coupled Hyperbolic PDE Systems	We develop a backstepping control design for a class of continuum systems of linear hyperbolic PDEs, described by a coupled system of an ensemble of rightward transporting PDEs and a (finite) system of $m$ leftward transporting PDEs. The key analysis challenge of the design is to establish well-posedness of the resulti...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	503,483
2412.03939	A robust quantum nonlinear solver based on the asymptotic numerical method	Quantum computing offers a promising new avenue for advancing computational methods in science and engineering. In this work, we introduce the quantum asymptotic numerical method, a novel quantum nonlinear solver that combines Taylor series expansions with quantum linear solvers to efficiently address nonlinear problem...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	514,186
2101.08449	Ensemble learning and iterative training (ELIT) machine learning: applications towards uncertainty quantification and automated experiment in atom-resolved microscopy	Deep learning has emerged as a technique of choice for rapid feature extraction across imaging disciplines, allowing rapid conversion of the data streams to spatial or spatiotemporal arrays of features of interest. However, applications of deep learning in experimental domains are often limited by the out-of-distributi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	216,323
2201.03601	Multiaxis nose-pointing-and-shooting in a biomimetic morphing-wing aircraft	Modern high-performance combat aircraft exceed conventional flight-envelope limits on maneuverability through the use of thrust vectoring, and so achieve supermaneuverability. With ongoing development of biomimetic unmanned aerial vehicles (UAVs), the potential for supermaneuverability through biomimetic mechanisms bec...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	274,887
2208.05553	Exploiting Neighborhood Interference with Low Order Interactions under Unit Randomized Design	Network interference, where the outcome of an individual is affected by the treatment assignment of those in their social network, is pervasive in real-world settings. However, it poses a challenge to estimating causal effects. We consider the task of estimating the total treatment effect (TTE), or the difference betwe...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	312,429
2110.07392	Provably Efficient Multi-Agent Reinforcement Learning with Fully Decentralized Communication	A challenge in reinforcement learning (RL) is minimizing the cost of sampling associated with exploration. Distributed exploration reduces sampling complexity in multi-agent RL (MARL). We investigate the benefits to performance in MARL when exploration is fully decentralized. Specifically, we consider a class of online...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	260,972
2311.12784	Optimality in Mean Estimation: Beyond Worst-Case, Beyond Sub-Gaussian, and Beyond $1+\alpha$ Moments	There is growing interest in improving our algorithmic understanding of fundamental statistical problems such as mean estimation, driven by the goal of understanding the limits of what we can extract from valuable data. The state of the art results for mean estimation in $\mathbb{R}$ are 1) the optimal sub-Gaussian mea...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	409,469
1106.0242	Nonapproximability Results for Partially Observable Markov Decision Processes	We show that for several variations of partially observable Markov decision processes, polynomial-time algorithms for finding control policies are unlikely to or simply don't have guarantees of finding policies within a constant factor or a constant summand of optimal. Here "unlikely" means "unless some complexity clas...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	10,650
2207.00725	Metacognitive Decision Making Framework for Multi-UAV Target Search Without Communication	This paper presents a new Metacognitive Decision Making (MDM) framework inspired by human-like metacognitive principles. The MDM framework is incorporated in unmanned aerial vehicles (UAVs) deployed for decentralized stochastic search without communication for detecting stationary targets (fixed/sudden pop-up) and dyna...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	305,862
2403.07561	Maximum Defective Clique Computation: Improved Time Complexities and Practical Performance	The concept of $k$-defective clique, a relaxation of clique by allowing up-to $k$ missing edges, has been receiving increasing interests recently. Although the problem of finding the maximum $k$-defective clique is NP-hard, several practical algorithms have been recently proposed in the literature, with kDC being the s...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	436,936
2409.03131	Well, that escalated quickly: The Single-Turn Crescendo Attack (STCA)	This paper introduces a new method for adversarial attacks on large language models (LLMs) called the Single-Turn Crescendo Attack (STCA). Building on the multi-turn crescendo attack method introduced by Russinovich, Salem, and Eldan (2024), which gradually escalates the context to provoke harmful responses, the STCA a...	false	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	485,926
2407.08340	SLRL: Structured Latent Representation Learning for Multi-view Clustering	In recent years, Multi-View Clustering (MVC) has attracted increasing attention for its potential to reduce the annotation burden associated with large datasets. The aim of MVC is to exploit the inherent consistency and complementarity among different views, thereby integrating information from multiple perspectives to...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	472,130
2204.03738	BankNote-Net: Open dataset for assistive universal currency recognition	Millions of people around the world have low or no vision. Assistive software applications have been developed for a variety of day-to-day tasks, including optical character recognition, scene identification, person recognition, and currency recognition. This last task, the recognition of banknotes from different denom...	true	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	290,415
1908.01349	JUMT at WMT2019 News Translation Task: A Hybrid approach to Machine Translation for Lithuanian to English	In the current work, we present a description of the system submitted to WMT 2019 News Translation Shared task. The system was created to translate news text from Lithuanian to English. To accomplish the given task, our system used a Word Embedding based Neural Machine Translation model to post edit the outputs generat...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	140,741
2401.08147	Machine Learning on Dynamic Graphs: A Survey on Applications	Dynamic graph learning has gained significant attention as it offers a powerful means to model intricate interactions among entities across various real-world and scientific domains. Notably, graphs serve as effective representations for diverse networks such as transportation, brain, social, and internet networks. Fur...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	421,783
2107.12866	Unsupervised Domain Adaptation for Hate Speech Detection Using a Data Augmentation Approach	Online harassment in the form of hate speech has been on the rise in recent years. Addressing the issue requires a combination of content moderation by people, aided by automatic detection methods. As content moderation is itself harmful to the people doing it, we desire to reduce the burden by improving the automatic ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	248,031
2212.01705	Breaking Down the Lockdown: The Causal Effects of Stay-At-Home Mandates on Uncertainty and Sentiments During the COVID-19 Pandemic	We study the causal effects of lockdown measures on uncertainty and sentiment on Twitter. To this end, we exploit the quasi-experimental framework created by the first COVID-19 lockdown in a high-income economy--the unexpected Italian lockdown in February 2020. We measure changes in public sentiment using deep learning...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	334,539
2308.03060	TOPIQ: A Top-down Approach from Semantics to Distortions for Image Quality Assessment	Image Quality Assessment (IQA) is a fundamental task in computer vision that has witnessed remarkable progress with deep neural networks. Inspired by the characteristics of the human visual system, existing methods typically use a combination of global and local representations (\ie, multi-scale features) to achieve su...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	383,883
2405.12262	Prompt Learning for Generalized Vehicle Routing	Neural combinatorial optimization (NCO) is a promising learning-based approach to solving various vehicle routing problems without much manual algorithm design. However, the current NCO methods mainly focus on the in-distribution performance, while the real-world problem instances usually come from different distributi...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	455,464
2403.08340	MorphoGear: An UAV with Multi-Limb Morphogenetic Gear for Rough-Terrain Locomotion	Robots able to run, fly, and grasp have a high potential to solve a wide scope of tasks and navigate in complex environments. Several mechatronic designs of such robots with adaptive morphologies are emerging. However, the task of landing on an uneven surface, traversing rough terrain, and manipulating objects still pr...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	437,301
1904.02496	Multi-Context Term Embeddings: the Use Case of Corpus-based Term Set Expansion	In this paper, we present a novel algorithm that combines multi-context term embeddings using a neural classifier and we test this approach on the use case of corpus-based term set expansion. In addition, we present a novel and unique dataset for intrinsic evaluation of corpus-based term set expansion algorithms. We sh...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	126,449
2206.07359	The Emotion is Not One-hot Encoding: Learning with Grayscale Label for Emotion Recognition in Conversation	In emotion recognition in conversation (ERC), the emotion of the current utterance is predicted by considering the previous context, which can be utilized in many natural language processing tasks. Although multiple emotions can coexist in a given sentence, most previous approaches take the perspective of a classificat...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	302,713
2305.14552	Sources of Hallucination by Large Language Models on Inference Tasks	Large Language Models (LLMs) are claimed to be capable of Natural Language Inference (NLI), necessary for applied tasks like question answering and summarization. We present a series of behavioral studies on several LLM families (LLaMA, GPT-3.5, and PaLM) which probe their behavior using controlled experiments. We esta...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	367,087
2210.07199	Self-Supervised Geometric Correspondence for Category-Level 6D Object Pose Estimation in the Wild	While 6D object pose estimation has wide applications across computer vision and robotics, it remains far from being solved due to the lack of annotations. The problem becomes even more challenging when moving to category-level 6D pose, which requires generalization to unseen instances. Current approaches are restricte...	false	false	false	false	false	false	true	true	false	false	false	true	false	false	false	false	false	false	323,611
2102.03710	HGAN: Hybrid Generative Adversarial Network	In this paper, we present a simple approach to train Generative Adversarial Networks (GANs) in order to avoid a \textit {mode collapse} issue. Implicit models such as GANs tend to generate better samples compared to explicit models that are trained on tractable data likelihood. However, GANs overlook the explicit data ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	218,837
1611.09149	Dynamic landscape models of coevolutionary games	Players of coevolutionary games may update not only their strategies but also their networks of interaction. Based on interpreting the payoff of players as fitness, dynamic landscape models are proposed. The modeling procedure is carried out for Prisoner's Dilemma (PD) and Snowdrift (SD) games that both use either birt...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	64,625
2102.10882	Conditional Positional Encodings for Vision Transformers	We propose a conditional positional encoding (CPE) scheme for vision Transformers. Unlike previous fixed or learnable positional encodings, which are pre-defined and independent of input tokens, CPE is dynamically generated and conditioned on the local neighborhood of the input tokens. As a result, CPE can easily gener...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	221,253
1802.07045	Latent RANSAC	We present a method that can evaluate a RANSAC hypothesis in constant time, i.e. independent of the size of the data. A key observation here is that correct hypotheses are tightly clustered together in the latent parameter domain. In a manner similar to the generalized Hough transform we seek to find this cluster, only...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	90,819
2409.06503	Advancements in Gesture Recognition Techniques and Machine Learning for Enhanced Human-Robot Interaction: A Comprehensive Review	In recent years robots have become an important part of our day-to-day lives with various applications. Human-robot interaction creates a positive impact in the field of robotics to interact and communicate with the robots. Gesture recognition techniques combined with machine learning algorithms have shown remarkable p...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	487,147
2212.04126	Complete Solution for Vehicle Re-ID in Surround-view Camera System	Vehicle re-identification (Re-ID) is a critical component of the autonomous driving perception system, and research in this area has accelerated in recent years. However, there is yet no perfect solution to the vehicle re-identification issue associated with the car's surround-view camera system. Our analysis identifie...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	335,338
2310.09413	ZeroSwap: Data-driven Optimal Market Making in DeFi	Automated Market Makers (AMMs) are major centers of matching liquidity supply and demand in Decentralized Finance. Their functioning relies primarily on the presence of liquidity providers (LPs) incentivized to invest their assets into a liquidity pool. However, the prices at which a pooled asset is traded is often mor...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	399,759
1805.06558	Recurrent Neural Network for Learning DenseDepth and Ego-Motion from Video	Learning-based, single-view depth estimation often generalizes poorly to unseen datasets. While learning-based, two-frame depth estimation solves this problem to some extent by learning to match features across frames, it performs poorly at large depth where the uncertainty is high. There exists few learning-based, mul...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	97,634
2106.14130	Continuous Control with Deep Reinforcement Learning for Autonomous Vessels	Maritime autonomous transportation has played a crucial role in the globalization of the world economy. Deep Reinforcement Learning (DRL) has been applied to automatic path planning to simulate vessel collision avoidance situations in open seas. End-to-end approaches that learn complex mappings directly from the input ...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	243,300
1806.07245	CAMIRADA: Cancer microRNA association discovery algorithm, a case study on breast cancer	In recent studies, non-coding protein RNAs have been identified as microRNA that can be used as biomarkers for early diagnosis and treatment of cancer, that decrease mortality in cancer. A microRNA may target hundreds or thousands of genes and a gene may regulate several microRNAs, so determining which microRNA is asso...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	100,877
2008.10849	LSTM Networks for Online Cross-Network Recommendations	Cross-network recommender systems use auxiliary information from multiple source networks to create holistic user profiles and improve recommendations in a target network. However, we find two major limitations in existing cross-network solutions that reduce overall recommender performance. Existing models (1) fail to ...	false	false	false	false	true	true	true	false	false	false	false	false	false	false	false	false	false	false	193,114
2403.13430	MTP: Advancing Remote Sensing Foundation Model via Multi-Task Pretraining	Foundation models have reshaped the landscape of Remote Sensing (RS) by enhancing various image interpretation tasks. Pretraining is an active research topic, encompassing supervised and self-supervised learning methods to initialize model weights effectively. However, transferring the pretrained models to downstream t...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	439,632
2010.10483	Sparse reconstruction in spin systems I: iid spins	For a sequence of Boolean functions $f_n : \{-1,1\}^{V_n} \longrightarrow \{-1,1\}$, defined on increasing configuration spaces of random inputs, we say that there is sparse reconstruction if there is a sequence of subsets $U_n \subseteq V_n$ of the coordinates satisfying $\|U_n\| = o(\|V_n\|)$ such that knowing the coordi...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	201,898
1806.05512	NetScore: Towards Universal Metrics for Large-scale Performance Analysis of Deep Neural Networks for Practical On-Device Edge Usage	Much of the focus in the design of deep neural networks has been on improving accuracy, leading to more powerful yet highly complex network architectures that are difficult to deploy in practical scenarios, particularly on edge devices such as mobile and other consumer devices given their high computational and memory ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	100,492
2501.04435	A Digital Shadow for Modeling, Studying and Preventing Urban Crime	Crime is one of the greatest threats to urban security. Around 80 percent of the world's population lives in countries with high levels of criminality. Most of the crimes committed in the cities take place in their urban environments. This paper presents the development and validation of a digital shadow platform for m...	false	false	false	true	true	false	false	false	false	false	false	false	false	false	true	false	false	false	523,222
2206.11658	An Optimization-Based User Scheduling Framework for mmWave Massive MU-MIMO Systems	We propose a novel user equipment (UE) scheduling framework for millimeter-wave (mmWave) massive multiuser (MU) multiple-input multiple-output (MIMO) wireless systems. Our framework determines (sub)sets of UEs that should transmit simultaneously in a given time slot by approximately solving a nonconvex optimization pro...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	304,329
2004.14302	Evaluating Dialogue Generation Systems via Response Selection	Existing automatic evaluation metrics for open-domain dialogue response generation systems correlate poorly with human evaluation. We focus on evaluating response generation systems via response selection. To evaluate systems properly via response selection, we propose the method to construct response selection test se...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	174,838
2405.08253	Thompson Sampling for Infinite-Horizon Discounted Decision Processes	We model a Markov decision process, parametrized by an unknown parameter, and study the asymptotic behavior of a sampling-based algorithm, called Thompson sampling. The standard definition of regret is not always suitable to evaluate a policy, especially when the underlying chain structure is general. We show that the ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	454,034
1607.04441	Efficient and Robust Pedestrian Detection using Deep Learning for Human-Aware Navigation	This paper addresses the problem of Human-Aware Navigation (HAN), using multi camera sensors to implement a vision-based person tracking system. The main contributions of this paper are as follows: a novel and efficient Deep Learning person detection and a standardization of human-aware constraints. In the first stage ...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	58,620
2312.10431	Continuous Diffusion for Mixed-Type Tabular Data	Score-based generative models (or diffusion models for short) have proven successful for generating text and image data. However, the adaption of this model family to tabular data of mixed-type has fallen short so far. In this paper, we propose CDTD, a Continuous Diffusion model for mixed-type Tabular Data. Specificall...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	416,166
2008.09866	Symbolic Semantic Segmentation and Interpretation of COVID-19 Lung Infections in Chest CT volumes based on Emergent Languages	The coronavirus disease (COVID-19) has resulted in a pandemic crippling the a breadth of services critical to daily life. Segmentation of lung infections in computerized tomography (CT) slices could be be used to improve diagnosis and understanding of COVID-19 in patients. Deep learning systems lack interpretability be...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	192,838
2211.04854	6G Mobile-Edge Empowered Metaverse: Requirements, Technologies, Challenges and Research Directions	The Metaverse has emerged as the successor of the conventional mobile internet to change people's lifestyles. It has strict visual and physical requirements to ensure an immersive experience (i.e., high visual quality, low motion-to-photon latency, and real-time tactile and control experience). However, the current tec...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	329,367
2311.03396	Differentially Private Pre-Trained Model Fusion using Decentralized Federated Graph Matching	Model fusion is becoming a crucial component in the context of model-as-a-service scenarios, enabling the delivery of high-quality model services to local users. However, this approach introduces privacy risks and imposes certain limitations on its applications. Ensuring secure model exchange and knowledge fusion among...	false	false	false	false	true	false	true	false	false	false	false	false	true	false	false	false	false	false	405,840
2303.02648	Comparative study of Transformer and LSTM Network with attention mechanism on Image Captioning	In a globalized world at the present epoch of generative intelligence, most of the manual labour tasks are automated with increased efficiency. This can support businesses to save time and money. A crucial component of generative intelligence is the integration of vision and language. Consequently, image captioning bec...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	349,438
2412.16339	Deliberative Alignment: Reasoning Enables Safer Language Models	As large-scale language models increasingly impact safety-critical domains, ensuring their reliable adherence to well-defined principles remains a fundamental challenge. We introduce Deliberative Alignment, a new paradigm that directly teaches the model safety specifications and trains it to explicitly recall and accur...	false	false	false	false	true	false	true	false	true	false	false	false	false	true	false	false	false	false	519,472
1910.08735	Tracking-Assisted Segmentation of Biological Cells	U-Net and its variants have been demonstrated to work sufficiently well in biological cell tracking and segmentation. However, these methods still suffer in the presence of complex processes such as collision of cells, mitosis and apoptosis. In this paper, we augment U-Net with Siamese matching-based tracking and propo...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	149,958
2412.13110	Improving Explainability of Sentence-level Metrics via Edit-level Attribution for Grammatical Error Correction	Various evaluation metrics have been proposed for Grammatical Error Correction (GEC), but many, particularly reference-free metrics, lack explainability. This lack of explainability hinders researchers from analyzing the strengths and weaknesses of GEC models and limits the ability to provide detailed feedback for user...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	518,165
1306.1557	Extending Universal Intelligence Models with Formal Notion of Representation	Solomonoff induction is known to be universal, but incomputable. Its approximations, namely, the Minimum Description (or Message) Length (MDL) principles, are adopted in practice in the efficient, but non-universal form. Recent attempts to bridge this gap leaded to development of the Representational MDL principle that...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	25,057
1703.07713	Hierarchical RNN with Static Sentence-Level Attention for Text-Based Speaker Change Detection	Speaker change detection (SCD) is an important task in dialog modeling. Our paper addresses the problem of text-based SCD, which differs from existing audio-based studies and is useful in various scenarios, for example, processing dialog transcripts where speaker identities are missing (e.g., OpenSubtitle), and enhanci...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	70,443
1909.03661	Extracting physical power plant parameters from historical behaviour	The information needed for fundamental modelling of the power markets -- the efficiency, start-up, fixed, and variable operating costs of each power plant -- is not publicly available. These parameters are usually estimated by considering the type of technology and the age of a power plant. We present a method to extra...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	144,568
2011.05537	Differentially Private Synthetic Data: Applied Evaluations and Enhancements	Machine learning practitioners frequently seek to leverage the most informative available data, without violating the data owner's privacy, when building predictive models. Differentially private data synthesis protects personal details from exposure, and allows for the training of differentially private machine learni...	false	false	false	false	true	false	true	false	false	false	false	false	true	true	false	false	false	false	205,952
1805.02788	ReGAN: RE[LAX\|BAR\|INFORCE] based Sequence Generation using GANs	Generative Adversarial Networks (GANs) have seen steep ascension to the peak of ML research zeitgeist in recent years. Mostly catalyzed by its success in the domain of image generation, the technique has seen wide range of adoption in a variety of other problem domains. Although GANs have had a lot of success in produc...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	96,913
2311.15268	Unlearning via Sparse Representations	Machine \emph{unlearning}, which involves erasing knowledge about a \emph{forget set} from a trained model, can prove to be costly and infeasible by existing techniques. We propose a nearly compute-free zero-shot unlearning technique based on a discrete representational bottleneck. We show that the proposed technique e...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	410,445
2004.13937	Revisiting Round-Trip Translation for Quality Estimation	Quality estimation (QE) is the task of automatically evaluating the quality of translations without human-translated references. Calculating BLEU between the input sentence and round-trip translation (RTT) was once considered as a metric for QE, however, it was found to be a poor predictor of translation quality. Recen...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	174,720
1506.00698	Statistical Machine Translation Features with Multitask Tensor Networks	We present a three-pronged approach to improving Statistical Machine Translation (SMT), building on recent success in the application of neural networks to SMT. First, we propose new features based on neural networks to model various non-local translation phenomena. Second, we augment the architecture of the neural net...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	43,700
1810.07273	Operationalizing Conflict and Cooperation between Automated Software Agents in Wikipedia: A Replication and Expansion of 'Even Good Bots Fight'	This paper replicates, extends, and refutes conclusions made in a study published in PLoS ONE ("Even Good Bots Fight"), which claimed to identify substantial levels of conflict between automated software agents (or bots) in Wikipedia using purely quantitative methods. By applying an integrative mixed-methods approach d...	true	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	false	110,602
2205.05888	How does Feedback Signal Quality Impact Effectiveness of Pseudo Relevance Feedback for Passage Retrieval?	Pseudo-Relevance Feedback (PRF) assumes that the top results retrieved by a first-stage ranker are relevant to the original query and uses them to improve the query representation for a second round of retrieval. This assumption however is often not correct: some or even all of the feedback documents may be irrelevant....	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	296,075
1912.01439	Generalization Error Bounds Via R\'enyi-, $f$-Divergences and Maximal Leakage	In this work, the probability of an event under some joint distribution is bounded by measuring it with the product of the marginals instead (which is typically easier to analyze) together with a measure of the dependence between the two random variables. These results find applications in adaptive data analysis, where...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	156,085
1111.2108	A criterion of simultaneously symmetrization and spectral finiteness for a finite set of real 2-by-2 matrices	In this paper, we consider the simultaneously symmetrization and spectral finiteness for a finite set of real 2-by-2 matrices.	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	12,968
2502.03506	Optimistic {\epsilon}-Greedy Exploration for Cooperative Multi-Agent Reinforcement Learning	The Centralized Training with Decentralized Execution (CTDE) paradigm is widely used in cooperative multi-agent reinforcement learning. However, due to the representational limitations of traditional monotonic value decomposition methods, algorithms can underestimate optimal actions, leading policies to suboptimal solu...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	530,754
2412.05271	Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling	We introduce InternVL 2.5, an advanced multimodal large language model (MLLM) series that builds upon InternVL 2.0, maintaining its core model architecture while introducing significant enhancements in training and testing strategies as well as data quality. In this work, we delve into the relationship between model sc...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	514,760
2006.10293	GAT-GMM: Generative Adversarial Training for Gaussian Mixture Models	Generative adversarial networks (GANs) learn the distribution of observed samples through a zero-sum game between two machine players, a generator and a discriminator. While GANs achieve great success in learning the complex distribution of image, sound, and text data, they perform suboptimally in learning multi-modal ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	182,836
1809.04505	Emo2Vec: Learning Generalized Emotion Representation by Multi-task Training	In this paper, we propose Emo2Vec which encodes emotional semantics into vectors. We train Emo2Vec by multi-task learning six different emotion-related tasks, including emotion/sentiment analysis, sarcasm classification, stress detection, abusive language classification, insult detection, and personality recognition. O...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	107,585
1909.05983	Content-Aware Unsupervised Deep Homography Estimation	Homography estimation is a basic image alignment method in many applications. It is usually conducted by extracting and matching sparse feature points, which are error-prone in low-light and low-texture images. On the other hand, previous deep homography approaches use either synthetic images for supervised learning or...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	145,255
2108.07058	FaPN: Feature-aligned Pyramid Network for Dense Image Prediction	Recent advancements in deep neural networks have made remarkable leap-forwards in dense image prediction. However, the issue of feature alignment remains as neglected by most existing approaches for simplicity. Direct pixel addition between upsampled and local features leads to feature maps with misaligned contexts tha...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	250,818
2308.11070	Temporal-Distributed Backdoor Attack Against Video Based Action Recognition	Deep neural networks (DNNs) have achieved tremendous success in various applications including video action recognition, yet remain vulnerable to backdoor attacks (Trojans). The backdoor-compromised model will mis-classify to the target class chosen by the attacker when a test instance (from a non-target class) is embe...	false	false	false	false	true	false	false	false	false	false	false	true	true	false	false	false	false	false	386,988
2312.01082	A Survey on Stability of Learning with Limited Labelled Data and its Sensitivity to the Effects of Randomness	Learning with limited labelled data, such as prompting, in-context learning, fine-tuning, meta-learning or few-shot learning, aims to effectively train a model using only a small amount of labelled samples. However, these approaches have been observed to be excessively sensitive to the effects of uncontrolled randomnes...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	412,303
1811.11866	A Review on Recommendation Systems: Context-aware to Social-based	The number of Internet users had grown rapidly enticing companies and cooperations to make full use of recommendation infrastructures. Consequently, online advertisement companies emerged to aid us in the presence of numerous items and users. Even as a user, you may find yourself drowned in a set of items that you thin...	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	false	114,866
1611.09827	Learning Features of Music from Scratch	This paper introduces a new large-scale music dataset, MusicNet, to serve as a source of supervision and evaluation of machine learning methods for music research. MusicNet consists of hundreds of freely-licensed classical music recordings by 10 composers, written for 11 instruments, together with instrument/note annot...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	64,725
2008.05772	CycleMorph: Cycle Consistent Unsupervised Deformable Image Registration	Image registration is a fundamental task in medical image analysis. Recently, deep learning based image registration methods have been extensively investigated due to their excellent performance despite the ultra-fast computational time. However, the existing deep learning methods still have limitation in the preservat...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	191,614
2108.07189	Masked Face Recognition Challenge: The WebFace260M Track Report	According to WHO statistics, there are more than 204,617,027 confirmed COVID-19 cases including 4,323,247 deaths worldwide till August 12, 2021. During the coronavirus epidemic, almost everyone wears a facial mask. Traditionally, face recognition approaches process mostly non-occluded faces, which include primary facia...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	250,855
2408.06024	Layer-Specific Optimization: Sensitivity Based Convolution Layers Basis Search	Deep neural network models have a complex architecture and are overparameterized. The number of parameters is more than the whole dataset, which is highly resource-consuming. This complicates their application and limits its usage on different devices. Reduction in the number of network parameters helps to reduce the s...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	true	480,049
2306.00614	Adaptation and Optimization of Automatic Speech Recognition (ASR) for the Maritime Domain in the Field of VHF Communication	This paper introduces a multilingual automatic speech recognizer (ASR) for maritime radio communi-cation that automatically converts received VHF radio signals into text. The challenges of maritime radio communication are described at first, and the deep learning architecture of marFM consisting of audio processing tec...	true	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	370,090
2207.10318	Efficient CNN Architecture Design Guided by Visualization	Modern efficient Convolutional Neural Networks(CNNs) always use Depthwise Separable Convolutions(DSCs) and Neural Architecture Search(NAS) to reduce the number of parameters and the computational complexity. But some inherent characteristics of networks are overlooked. Inspired by visualizing feature maps and N$\times$...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	309,225
2206.06694	ISLES 2022: A multi-center magnetic resonance imaging stroke lesion segmentation dataset	Magnetic resonance imaging (MRI) is a central modality for stroke imaging. It is used upon patient admission to make treatment decisions such as selecting patients for intravenous thrombolysis or endovascular therapy. MRI is later used in the duration of hospital stay to predict outcome by visualizing infarct core size...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	302,464
2402.02456	tnGPS: Discovering Unknown Tensor Network Structure Search Algorithms via Large Language Models (LLMs)	Tensor networks are efficient for extremely high-dimensional representation, but their model selection, known as tensor network structure search (TN-SS), is a challenging problem. Although several works have targeted TN-SS, most existing algorithms are manually crafted heuristics with poor performance, suffering from t...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	426,572
1206.6850	Visualization of Collaborative Data	Collaborative data consist of ratings relating two distinct sets of objects: users and items. Much of the work with such data focuses on filtering: predicting unknown ratings for pairs of users and items. In this paper we focus on the problem of visualizing the information. Given all of the ratings, our task is to embe...	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	17,075
2106.03783	An Information-theoretic Approach to Distribution Shifts	Safely deploying machine learning models to the real world is often a challenging process. Models trained with data obtained from a specific geographic location tend to fail when queried with data obtained elsewhere, agents trained in a simulation can struggle to adapt when deployed in the real world or novel environme...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	239,450
2109.11610	SPNet: Multi-Shell Kernel Convolution for Point Cloud Semantic Segmentation	Feature encoding is essential for point cloud analysis. In this paper, we propose a novel point convolution operator named Shell Point Convolution (SPConv) for shape encoding and local context learning. Specifically, SPConv splits 3D neighborhood space into shells, aggregates local features on manually designed kernel ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	256,993
2105.00525	Planning for Proactive Assistance in Environments with Partial Observability	This paper addresses the problem of synthesizing the behavior of an AI agent that provides proactive task assistance to a human in settings like factory floors where they may coexist in a common environment. Unlike in the case of requested assistance, the human may not be expecting proactive assistance and hence it is ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	233,247
1906.08312	Calibrated Model-Based Deep Reinforcement Learning	Estimates of predictive uncertainty are important for accurate model-based planning and reinforcement learning. However, predictive uncertainties---especially ones derived from modern deep learning systems---can be inaccurate and impose a bottleneck on performance. This paper explores which uncertainties are needed for...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	135,830
2405.17430	Matryoshka Multimodal Models	Large Multimodal Models (LMMs) such as LLaVA have shown strong performance in visual-linguistic reasoning. These models first embed images into a fixed large number of visual tokens and then feed them into a Large Language Model (LLM). However, this design causes an excessive number of tokens for dense visual scenarios...	false	false	false	false	true	false	true	false	true	false	false	true	false	false	false	false	false	false	457,914
1909.12642	HateMonitors: Language Agnostic Abuse Detection in Social Media	Reducing hateful and offensive content in online social media pose a dual problem for the moderators. On the one hand, rigid censorship on social media cannot be imposed. On the other, the free flow of such content cannot be allowed. Hence, we require efficient abusive language detection system to detect such harmful c...	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	false	false	false	147,181
2403.16483	Automatic Construction of a Large-Scale Corpus for Geoparsing Using Wikipedia Hyperlinks	Geoparsing is the task of estimating the latitude and longitude (coordinates) of location expressions in texts. Geoparsing must deal with the ambiguity of the expressions that indicate multiple locations with the same notation. For evaluating geoparsing systems, several corpora have been proposed in previous work. Howe...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	441,064
2307.15723	Agent-Based Model: Simulating a Virus Expansion Based on the Acceptance of Containment Measures	Compartmental epidemiological models categorize individuals based on their disease status, such as the SEIRD model (Susceptible-Exposed-Infected-Recovered-Dead). These models determine the parameters that influence the magnitude of an outbreak, such as contagion and recovery rates. However, they don't account for indiv...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	382,353
2205.01458	Frequency-Selective Geometry Upsampling of Point Clouds	The demand for high-resolution point clouds has increased throughout the last years. However, capturing high-resolution point clouds is expensive and thus, frequently replaced by upsampling of low-resolution data. Most state-of-the-art methods are either restricted to a rastered grid, incorporate normal vectors, or are...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	294,590
1911.11060	A Survey on Adversarial Information Retrieval on the Web	This survey paper discusses different forms of malicious techniques that can affect how an information retrieval model retrieves documents for a query and their remedies.	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	155,006
2203.15590	Heuristic-based Inter-training to Improve Few-shot Multi-perspective Dialog Summarization	Many organizations require their customer-care agents to manually summarize their conversations with customers. These summaries are vital for decision making purposes of the organizations. The perspective of the summary that is required to be created depends on the application of the summaries. With this work, we study...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	288,466
2412.02879	Pairwise Spatiotemporal Partial Trajectory Matching for Co-movement Analysis	Spatiotemporal pairwise movement analysis involves identifying shared geographic-based behaviors between individuals within specific time frames. Traditionally, this task relies on sequence modeling and behavior analysis techniques applied to tabular or video-based data, but these methods often lack interpretability an...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	513,719
1602.08254	Theoretical Analysis of the $k$-Means Algorithm - A Survey	The $k$-means algorithm is one of the most widely used clustering heuristics. Despite its simplicity, analyzing its running time and quality of approximation is surprisingly difficult and can lead to deep insights that can be used to improve the algorithm. In this paper we survey the recent results in this direction as...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	52,622
2410.21299	TV-3DG: Mastering Text-to-3D Customized Generation with Visual Prompt	In recent years, advancements in generative models have significantly expanded the capabilities of text-to-3D generation. Many approaches rely on Score Distillation Sampling (SDS) technology. However, SDS struggles to accommodate multi-condition inputs, such as text and visual prompts, in customized generation tasks. T...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	503,182
2112.05493	Network Compression via Central Filter	Neural network pruning has remarkable performance for reducing the complexity of deep network models. Recent network pruning methods usually focused on removing unimportant or redundant filters in the network. In this paper, by exploring the similarities between feature maps, we propose a novel filter pruning method, C...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	270,859
0806.4484	On empirical meaning of randomness with respect to a real parameter	We study the empirical meaning of randomness with respect to a family of probability distributions $P_\theta$, where $\theta$ is a real parameter, using algorithmic randomness theory. In the case when for a computable probability distribution $P_\theta$ an effectively strongly consistent estimate exists, we show that t...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	1,995
2104.04450	Unsupervised Class-Incremental Learning Through Confusion	While many works on Continual Learning have shown promising results for mitigating catastrophic forgetting, they have relied on supervised training. To successfully learn in a label-agnostic incremental setting, a model must distinguish between learned and novel classes to properly include samples for training. We intr...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	229,393
2309.06248	Rethinking Evaluation Metric for Probability Estimation Models Using Esports Data	Probability estimation models play an important role in various fields, such as weather forecasting, recommendation systems, and sports analysis. Among several models estimating probabilities, it is difficult to evaluate which model gives reliable probabilities since the ground-truth probabilities are not available. Th...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	391,360
1207.2488	Kernelized Supervised Dictionary Learning	In this paper, we propose supervised dictionary learning (SDL) by incorporating information on class labels into the learning of the dictionary. To this end, we propose to learn the dictionary in a space where the dependency between the signals and their corresponding labels is maximized. To maximize this dependency, t...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	17,387

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