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
2101.10480	Symmetric Monoidal Categories with Attributes	When designing plans in engineering, it is often necessary to consider attributes associated to objects, e.g. the location of a robot. Our aim in this paper is to incorporate attributes into existing categorical formalisms for planning, namely those based on symmetric monoidal categories and string diagrams. To accompl...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	216,948
1707.03386	DeepCodec: Adaptive Sensing and Recovery via Deep Convolutional Neural Networks	In this paper we develop a novel computational sensing framework for sensing and recovering structured signals. When trained on a set of representative signals, our framework learns to take undersampled measurements and recover signals from them using a deep convolutional neural network. In other words, it learns a tra...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	76,859
1203.6122	Diffusion of Real-Time Information in Social-Physical Networks	We study the diffusion behavior of real-time information. Typically, real-time information is valuable only for a limited time duration, and hence needs to be delivered before its "deadline." Therefore, real-time information is much easier to spread among a group of people with frequent interactions than between isolat...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	15,148
2406.04690	Higher-order Structure Based Anomaly Detection on Attributed Networks	Anomaly detection (such as telecom fraud detection and medical image detection) has attracted the increasing attention of people. The complex interaction between multiple entities widely exists in the network, which can reflect specific human behavior patterns. Such patterns can be modeled by higher-order network struc...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	461,797
2403.03305	Best of Both Worlds: A Pliable and Generalizable Neuro-Symbolic Approach for Relation Classification	This paper introduces a novel neuro-symbolic architecture for relation classification (RC) that combines rule-based methods with contemporary deep learning techniques. This approach capitalizes on the strengths of both paradigms: the adaptability of rule-based systems and the generalization power of neural networks. Ou...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	435,139
2403.15601	From Guidelines to Governance: A Study of AI Policies in Education	Emerging technologies like generative AI tools, including ChatGPT, are increasingly utilized in educational settings, offering innovative approaches to learning while simultaneously posing new challenges. This study employs a survey methodology to examine the policy landscape concerning these technologies, drawing insi...	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	440,654
2409.02281	K-Origins: Better Colour Quantification for Neural Networks	K-Origins is a neural network layer designed to improve image-based network performances when learning colour, or intensities, is beneficial. Over 250 encoder-decoder convolutional networks are trained and tested on 16-bit synthetic data, demonstrating that K-Origins improves semantic segmentation accuracy in two scena...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	485,635
2308.05404	Enhancing Low-light Light Field Images with A Deep Compensation Unfolding Network	This paper presents a novel and interpretable end-to-end learning framework, called the deep compensation unfolding network (DCUNet), for restoring light field (LF) images captured under low-light conditions. DCUNet is designed with a multi-stage architecture that mimics the optimization process of solving an inverse i...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	384,781
2407.09837	Impedance Measurement of Rolling Bearings Using an unbalanced AC Wheatstone Bridge	Industry 4.0 drives the demand for cost-efficient and reliable process data and condition monitoring. Therefore, visualizing the state of tribological contacts becomes important, as they are regularly found in the center of many applications. Utilizing rolling element bearings as sensors and monitoring their health by ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	472,740
1609.05296	Development of a Fuzzy Expert System based Liveliness Detection Scheme for Biometric Authentication	Liveliness detection acts as a safe guard against spoofing attacks. Most of the researchers used vision based techniques to detect liveliness of the user, but they are highly sensitive to illumination effects. Therefore it is very hard to design a system, which will work robustly under all circumstances. Literature sho...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	61,117
2304.02099	Coarse Grained FLS-based Processor with Prognostic Malfunction Feature for UAM Drones using FPGA	Many overall safety factors need to be considered in the next generation of Urban Air Mobility (UAM) systems and addressing these can become the anchor point for such technology to reach consent for worldwide application. On the other hand, fulfilling the safety requirements from an exponential increase of prolific UAM...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	356,319
2301.04647	EXIF as Language: Learning Cross-Modal Associations Between Images and Camera Metadata	We learn a visual representation that captures information about the camera that recorded a given photo. To do this, we train a multimodal embedding between image patches and the EXIF metadata that cameras automatically insert into image files. Our model represents this metadata by simply converting it to text and then...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	340,130
2007.15652	Canopy Density Estimation in Perennial Horticulture Crops Using 3D Spinning Lidar SLAM	We propose a novel, canopy density estimation solution using a 3D ray cloud representation for perennial horticultural crops at the field scale. To attain high spatial and temporal fidelity in field conditions, we propose the application of continuous-time 3D SLAM (Simultaneous Localisation and Mapping) to a spinning l...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	189,724
1706.09076	A Pig, an Angel and a Cactus Walk Into a Blender: A Descriptive Approach to Visual Blending	A descriptive approach for automatic generation of visual blends is presented. The implemented system, the Blender, is composed of two components: the Mapper and the Visual Blender. The approach uses structured visual representations along with sets of visual relations which describe how the elements (in which the visu...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	76,084
0810.0532	Three New Complexity Results for Resource Allocation Problems	We prove the following results for task allocation of indivisible resources: - The problem of finding a leximin-maximal resource allocation is in P if the agents have max-utility functions and atomic demands. - Deciding whether a resource allocation is Pareto-optimal is coNP-complete for agents with (1-)additive ut...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	true	2,444
2210.11374	Meeting Decision Tracker: Making Meeting Minutes with De-Contextualized Utterances	Meetings are a universal process to make decisions in business and project collaboration. The capability to automatically itemize the decisions in daily meetings allows for extensive tracking of past discussions. To that end, we developed Meeting Decision Tracker, a prototype system to construct decision items comprisi...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	325,285
2401.03221	MirrorDiffusion: Stabilizing Diffusion Process in Zero-shot Image Translation by Prompts Redescription and Beyond	Recently, text-to-image diffusion models become a new paradigm in image processing fields, including content generation, image restoration and image-to-image translation. Given a target prompt, Denoising Diffusion Probabilistic Models (DDPM) are able to generate realistic yet eligible images. With this appealing proper...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	420,026
2111.10570	Improving Spectral Efficiency of Wireless Networks through Democratic Spectrum Sharing	Wireless devices need spectrum to communicate. With the increase in the number of devices competing for the same spectrum, it has become nearly impossible to support the throughput requirements of all the devices through current spectrum sharing methods. In this work, we look at the problem of spectrum resource content...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	true	false	false	true	267,366
2008.10753	Evaluating Nonlinear Decision Trees for Binary Classification Tasks with Other Existing Methods	Classification of datasets into two or more distinct classes is an important machine learning task. Many methods are able to classify binary classification tasks with a very high accuracy on test data, but cannot provide any easily interpretable explanation for users to have a deeper understanding of reasons for the sp...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	193,077
2312.05519	Isomorphic-Consistent Variational Graph Auto-Encoders for Multi-Level Graph Representation Learning	Graph representation learning is a fundamental research theme and can be generalized to benefit multiple downstream tasks from the node and link levels to the higher graph level. In practice, it is desirable to develop task-agnostic general graph representation learning methods that are typically trained in an unsuperv...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	414,121
2010.16344	Marginalised Gaussian Processes with Nested Sampling	Gaussian Process (GPs) models are a rich distribution over functions with inductive biases controlled by a kernel function. Learning occurs through the optimisation of kernel hyperparameters using the marginal likelihood as the objective. This classical approach known as Type-II maximum likelihood (ML-II) yields point ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	204,043
1506.07424	Simulating the Effects of Various Road Infrastructure Improvements to Vehicular Traffic in a Busy Three-road Fork	Using microsimulations of vehicular dynamics, we studied the effects of several proposed infrastructure developments to the mean travel delay time~$\Delta$ and mean speed~$\Sigma$ of vehicles passing a busy three-road fork, particularly in the non-signalized roundabout junction of Lower Bicutan, Taguig City, Metro Mani...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	44,517
2106.14577	Privacy-Preserving Image Acquisition Using Trainable Optical Kernel	Preserving privacy is a growing concern in our society where sensors and cameras are ubiquitous. In this work, for the first time, we propose a trainable image acquisition method that removes the sensitive identity revealing information in the optical domain before it reaches the image sensor. The method benefits from ...	false	false	false	false	false	false	true	false	false	false	false	true	true	false	false	false	false	false	243,451
2106.03614	Adversarial Attack and Defense in Deep Ranking	Deep Neural Network classifiers are vulnerable to adversarial attack, where an imperceptible perturbation could result in misclassification. However, the vulnerability of DNN-based image ranking systems remains under-explored. In this paper, we propose two attacks against deep ranking systems, i.e., Candidate Attack an...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	239,381
1410.5894	Vehicle Detection and Tracking Techniques: A Concise Review	Vehicle detection and tracking applications play an important role for civilian and military applications such as in highway traffic surveillance control, management and urban traffic planning. Vehicle detection process on road are used for vehicle tracking, counts, average speed of each individual vehicle, traffic ana...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	36,950
2302.03549	An Achievable and Analytic Solution to Information Bottleneck for Gaussian Mixtures	In this paper, we study a remote source coding scenario in which binary phase shift keying (BPSK) modulation sources are corrupted by additive white Gaussian noise (AWGN). An intermediate node, such as a relay, receives these observations and performs additional compression to balance complexity and relevance. This pro...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	344,381
2411.00462	Target-Guided Adversarial Point Cloud Transformer Towards Recognition Against Real-world Corruptions	Achieving robust 3D perception in the face of corrupted data presents an challenging hurdle within 3D vision research. Contemporary transformer-based point cloud recognition models, albeit advanced, tend to overfit to specific patterns, consequently undermining their robustness against corruption. In this work, we intr...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	504,608
1711.04068	Reuters Tracer: Toward Automated News Production Using Large Scale Social Media Data	To deal with the sheer volume of information and gain competitive advantage, the news industry has started to explore and invest in news automation. In this paper, we present Reuters Tracer, a system that automates end-to-end news production using Twitter data. It is capable of detecting, classifying, annotating, and d...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	84,324
2501.17963	Physics-Grounded Differentiable Simulation for Soft Growing Robots	Soft-growing robots (i.e., vine robots) are a promising class of soft robots that allow for navigation and growth in tightly confined environments. However, these robots remain challenging to model and control due to the complex interplay of the inflated structure and inextensible materials, which leads to obstacles fo...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	528,516
2404.08433	MSSTNet: A Multi-Scale Spatio-Temporal CNN-Transformer Network for Dynamic Facial Expression Recognition	Unlike typical video action recognition, Dynamic Facial Expression Recognition (DFER) does not involve distinct moving targets but relies on localized changes in facial muscles. Addressing this distinctive attribute, we propose a Multi-Scale Spatio-temporal CNN-Transformer network (MSSTNet). Our approach takes spatial ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	446,239
1005.0063	Large Margin Multiclass Gaussian Classification with Differential Privacy	As increasing amounts of sensitive personal information is aggregated into data repositories, it has become important to develop mechanisms for processing the data without revealing information about individual data instances. The differential privacy model provides a framework for the development and theoretical analy...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	6,362
2407.18472	FedUD: Exploiting Unaligned Data for Cross-Platform Federated Click-Through Rate Prediction	Click-through rate (CTR) prediction plays an important role in online advertising platforms. Most existing methods use data from the advertising platform itself for CTR prediction. As user behaviors also exist on many other platforms, e.g., media platforms, it is beneficial to further exploit such complementary informa...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	476,385
2303.04092	CroCoSum: A Benchmark Dataset for Cross-Lingual Code-Switched Summarization	Cross-lingual summarization (CLS) has attracted increasing interest in recent years due to the availability of large-scale web-mined datasets and the advancements of multilingual language models. However, given the rareness of naturally occurring CLS resources, the majority of datasets are forced to rely on translation...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	349,955
2010.01274	Assisting the Adversary to Improve GAN Training	Some of the most popular methods for improving the stability and performance of GANs involve constraining or regularizing the discriminator. In this paper we consider a largely overlooked regularization technique which we refer to as the Adversary's Assistant (AdvAs). We motivate this using a different perspective to t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	198,590
2401.15476	To Burst or Not to Burst: Generating and Quantifying Improbable Text	While large language models (LLMs) are extremely capable at text generation, their outputs are still distinguishable from human-authored text. We explore this separation across many metrics over text, many sampling techniques, many types of text data, and across two popular LLMs, LLaMA and Vicuna. Along the way, we int...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	424,460
2105.01136	Learning Good State and Action Representations via Tensor Decomposition	The transition kernel of a continuous-state-action Markov decision process (MDP) admits a natural tensor structure. This paper proposes a tensor-inspired unsupervised learning method to identify meaningful low-dimensional state and action representations from empirical trajectories. The method exploits the MDP's tensor...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	233,436
1905.06184	Extensions to Justification Theory	Justification theory is a unifying framework for semantics of non-monotonic logics. It is built on the notion of a justification, which intuitively is a graph that explains the truth value of certain facts in a structure. Knowledge representation languages covered by justification theory include logic programs, argumen...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	130,917
2205.13976	Hybrid Offline-Online Design for Reconfigurable Intelligent Surface Aided UAV Communication	This letter considers the reconfigurable intelligent surface (RIS)-aided unmanned aerial vehicle (UAV) communication systems in urban areas under the general Rician fading channel. A hybrid offline-online design is proposed to improve the system performance by leveraging both the statistical channel state information (...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	299,167
2004.13537	Correlated randomly growing graphs	We introduce a new model of correlated randomly growing graphs and study the fundamental questions of detecting correlation and estimating aspects of the correlated structure. The model is simple and starts with any model of randomly growing graphs, such as uniform attachment (UA) or preferential attachment (PA). Given...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	174,581
1910.13445	G2SAT: Learning to Generate SAT Formulas	The Boolean Satisfiability (SAT) problem is the canonical NP-complete problem and is fundamental to computer science, with a wide array of applications in planning, verification, and theorem proving. Developing and evaluating practical SAT solvers relies on extensive empirical testing on a set of real-world benchmark f...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	true	151,393
1203.0160	Scaling Datalog for Machine Learning on Big Data	In this paper, we present the case for a declarative foundation for data-intensive machine learning systems. Instead of creating a new system for each specific flavor of machine learning task, or hardcoding new optimizations, we argue for the use of recursive queries to program a variety of machine learning systems. By...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	true	14,678
1406.3387	The Interplay Between Dynamics and Networks: Centrality, Communities, and Cheeger Inequality	We study the interplay between a dynamic process and the structure of the network on which it is defined. Specifically, we examine the impact of this interaction on the quality-measure of network clusters and node centrality. This enables us to effectively identify network communities and important nodes participating ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	33,840
2402.07937	A Physiological Sensor-Based Android Application Synchronized with a Driving Simulator for Driver Monitoring	In this paper, we present an Android application to control and monitor the physiological sensors from the Shimmer platform and its synchronized working with a driving simulator. The Android app can monitor drivers and their parameters can be used to analyze the relation between their physiological states and driving p...	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	428,905
2010.05103	On the Importance of Adaptive Data Collection for Extremely Imbalanced Pairwise Tasks	Many pairwise classification tasks, such as paraphrase detection and open-domain question answering, naturally have extreme label imbalance (e.g., $99.99\%$ of examples are negatives). In contrast, many recent datasets heuristically choose examples to ensure label balance. We show that these heuristics lead to trained ...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	199,986
2001.06959	Non-Orthogonal Multiple Access with Wireless Caching for 5G-Enabled Vehicular Networks	The proliferation of connected vehicles along with the high demand for rich multimedia services constitute key challenges for the emerging 5G-enabled vehicular networks. These challenges include, but are not limited to, high spectral efficiency and low latency requirements. Recently, the integration of cache-enabled ne...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	160,937
2103.08469	Developing an Underwater Network of Ocean Observation Systems with Digital Twin Prototypes -- A Field Report from the Baltic Sea	During the research cruise AL547 with RV ALKOR (October 20-31, 2020), a collaborative underwater network of ocean observation systems was deployed in Boknis Eck (SW Baltic Sea, German exclusive economic zone (EEZ)) in the context of the project ARCHES (Autonomous Robotic Networks to Help Modern Societies). This network...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	224,913
1507.04437	A Deep Hashing Learning Network	Hashing-based methods seek compact and efficient binary codes that preserve the neighborhood structure in the original data space. For most existing hashing methods, an image is first encoded as a vector of hand-crafted visual feature, followed by a hash projection and quantization step to get the compact binary vector...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	45,173
2412.19215	Optimizing Fantasy Sports Team Selection with Deep Reinforcement Learning	Fantasy sports, particularly fantasy cricket, have garnered immense popularity in India in recent years, offering enthusiasts the opportunity to engage in strategic team-building and compete based on the real-world performance of professional athletes. In this paper, we address the challenge of optimizing fantasy crick...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	520,761
2306.15260	Linear One-Bit Precoding in Massive MIMO: Asymptotic SEP Analysis and Optimization	This paper focuses on the analysis and optimization of a class of linear one-bit precoding schemes for a downlink massive MIMO system under Rayleigh fading channels. The considered class of linear one-bit precoding is fairly general, including the well-known matched filter (MF) and zero-forcing (ZF) precoding schemes a...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	375,965
2302.08727	Building Shortcuts between Distant Nodes with Biaffine Mapping for Graph Convolutional Networks	Multiple recent studies show a paradox in graph convolutional networks (GCNs), that is, shallow architectures limit the capability of learning information from high-order neighbors, while deep architectures suffer from over-smoothing or over-squashing. To enjoy the simplicity of shallow architectures and overcome their...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	346,161
1911.07013	Understanding and Improving Layer Normalization	Layer normalization (LayerNorm) is a technique to normalize the distributions of intermediate layers. It enables smoother gradients, faster training, and better generalization accuracy. However, it is still unclear where the effectiveness stems from. In this paper, our main contribution is to take a step further in und...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	153,697
2203.10375	Design and Development of a Research Oriented Low Cost Robotics Platform with a Novel Dynamic Global Path Planning Approach	Autonomous navigation systems based on computer vision sensors often require sophisticated robotics platforms which are very expensive. This poses a barrier for the implementation and testing of complex localization, mapping, and navigation algorithms that are vital in robotics applications. Addressing this issue, in t...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	286,512
2209.12866	SAPA: Similarity-Aware Point Affiliation for Feature Upsampling	We introduce point affiliation into feature upsampling, a notion that describes the affiliation of each upsampled point to a semantic cluster formed by local decoder feature points with semantic similarity. By rethinking point affiliation, we present a generic formulation for generating upsampling kernels. The kernels ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	319,688
2405.05831	Common information in well-mixing graphs and applications to information-theoretic cryptography	We study the connection between mixing properties for bipartite graphs and materialization of the mutual information in one-shot settings. We show that mixing properties of a graph imply impossibility to extract the mutual information shared by the ends of an edge randomly sampled in the graph. We apply these impossibi...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	453,081
2412.16202	Aspect-Based Few-Shot Learning	We generalize the formulation of few-shot learning by introducing the concept of an aspect. In the traditional formulation of few-shot learning, there is an underlying assumption that a single "true" label defines the content of each data point. This label serves as a basis for the comparison between the query object a...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	519,408
2402.15960	Budget-Constrained Tool Learning with Planning	Despite intensive efforts devoted to tool learning, the problem of budget-constrained tool learning, which focuses on resolving user queries within a specific budget constraint, has been widely overlooked. This paper proposes a novel method for budget-constrained tool learning. Our approach involves creating a preferab...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	432,354
1603.04026	A comprehensive study of sparse codes on abnormality detection	Sparse representation has been applied successfully in abnormal event detection, in which the baseline is to learn a dictionary accompanied by sparse codes. While much emphasis is put on discriminative dictionary construction, there are no comparative studies of sparse codes regarding abnormality detection. We comprehe...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	53,182
2408.14991	Speech Recognition Transformers: Topological-lingualism Perspective	Transformers have evolved with great success in various artificial intelligence tasks. Thanks to our recent prevalence of self-attention mechanisms, which capture long-term dependency, phenomenal outcomes in speech processing and recognition tasks have been produced. The paper presents a comprehensive survey of transfo...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	483,758
2003.05864	Random NOMA With Cross-Slot Successive Interference Cancellation Packet Recovery	Conventional power-domain non-orthogonal multiple access (NOMA) relies on precise power control, which requires real-time channel state information at transmitters. This requirement severely limits its application to future wireless communication systems. To address this problem, we consider NOMA without power allocati...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	167,969
2303.12997	FER-former: Multi-modal Transformer for Facial Expression Recognition	The ever-increasing demands for intuitive interactions in Virtual Reality has triggered a boom in the realm of Facial Expression Recognition (FER). To address the limitations in existing approaches (e.g., narrow receptive fields and homogenous supervisory signals) and further cement the capacity of FER tools, a novel m...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	353,492
2105.06229	Reciprocal Feature Learning via Explicit and Implicit Tasks in Scene Text Recognition	Text recognition is a popular topic for its broad applications. In this work, we excavate the implicit task, character counting within the traditional text recognition, without additional labor annotation cost. The implicit task plays as an auxiliary branch for complementing the sequential recognition. We design a two-...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	235,060
2411.00965	SPOT: SE(3) Pose Trajectory Diffusion for Object-Centric Manipulation	We introduce SPOT, an object-centric imitation learning framework. The key idea is to capture each task by an object-centric representation, specifically the SE(3) object pose trajectory relative to the target. This approach decouples embodiment actions from sensory inputs, facilitating learning from various demonstrat...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	504,851
1807.08089	Phonetic-and-Semantic Embedding of Spoken Words with Applications in Spoken Content Retrieval	Word embedding or Word2Vec has been successful in offering semantics for text words learned from the context of words. Audio Word2Vec was shown to offer phonetic structures for spoken words (signal segments for words) learned from signals within spoken words. This paper proposes a two-stage framework to perform phoneti...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	103,452
1911.13218	ModelHub.AI: Dissemination Platform for Deep Learning Models	Recent advances in artificial intelligence research have led to a profusion of studies that apply deep learning to problems in image analysis and natural language processing among others. Additionally, the availability of open-source computational frameworks has lowered the barriers to implementing state-of-the-art met...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	155,613
2402.17535	Multimodal Learned Sparse Retrieval with Probabilistic Expansion Control	Learned sparse retrieval (LSR) is a family of neural methods that encode queries and documents into sparse lexical vectors that can be indexed and retrieved efficiently with an inverted index. We explore the application of LSR to the multi-modal domain, with a focus on text-image retrieval. While LSR has seen success i...	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	433,040
2010.10453	Modeling Content and Context with Deep Relational Learning	Building models for realistic natural language tasks requires dealing with long texts and accounting for complicated structural dependencies. Neural-symbolic representations have emerged as a way to combine the reasoning capabilities of symbolic methods, with the expressiveness of neural networks. However, most of the ...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	201,885
1909.12931	Revenue allocation in Formula One: a pairwise comparison approach	A model is proposed to allocate Formula One World Championship prize money among the constructors. The methodology is based on pairwise comparison matrices, allows for the use of any weighting method, and makes possible to tune the level of inequality. We introduce an axiom called scale invariance, which requires the r...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	147,257
1703.07872	Random Features for Compositional Kernels	We describe and analyze a simple random feature scheme (RFS) from prescribed compositional kernels. The compositional kernels we use are inspired by the structure of convolutional neural networks and kernels. The resulting scheme yields sparse and efficiently computable features. Each random feature can be represented ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	70,468
2210.12587	Model ensemble instead of prompt fusion: a sample-specific knowledge transfer method for few-shot prompt tuning	Prompt tuning approaches, which learn task-specific soft prompts for a downstream task conditioning on frozen pre-trained models, have attracted growing interest due to its parameter efficiency. With large language models and sufficient training data, prompt tuning performs comparably to full-model tuning. However, wit...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	325,809
2403.02803	Towards Robust Federated Learning via Logits Calibration on Non-IID Data	Federated learning (FL) is a privacy-preserving distributed management framework based on collaborative model training of distributed devices in edge networks. However, recent studies have shown that FL is vulnerable to adversarial examples (AEs), leading to a significant drop in its performance. Meanwhile, the non-ind...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	434,953
2108.12105	Full Attention Bidirectional Deep Learning Structure for Single Channel Speech Enhancement	As the cornerstone of other important technologies, such as speech recognition and speech synthesis, speech enhancement is a critical area in audio signal processing. In this paper, a new deep learning structure for speech enhancement is demonstrated. The model introduces a "full" attention mechanism to a bidirectional...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	252,389
1705.03386	Cell Tracking via Proposal Generation and Selection	Microscopy imaging plays a vital role in understanding many biological processes in development and disease. The recent advances in automation of microscopes and development of methods and markers for live cell imaging has led to rapid growth in the amount of image data being captured. To efficiently and reliably extra...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	73,173
2501.15063	Cross-modal Context Fusion and Adaptive Graph Convolutional Network for Multimodal Conversational Emotion Recognition	Emotion recognition has a wide range of applications in human-computer interaction, marketing, healthcare, and other fields. In recent years, the development of deep learning technology has provided new methods for emotion recognition. Prior to this, many emotion recognition methods have been proposed, including multim...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	527,385
2306.16255	Theory and applications of the Sum-Of-Squares technique	The Sum-of-Squares (SOS) approximation method is a technique used in optimization problems to derive lower bounds on the optimal value of an objective function. By representing the objective function as a sum of squares in a feature space, the SOS method transforms non-convex global optimization problems into solvable ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	376,320
2211.02162	Time-aware Prompting for Text Generation	In this paper, we study the effects of incorporating timestamps, such as document creation dates, into generation systems. Two types of time-aware prompts are investigated: (1) textual prompts that encode document timestamps in natural language sentences; and (2) linear prompts that convert timestamps into continuous v...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	328,491
2104.13946	Motion-guided Non-local Spatial-Temporal Network for Video Crowd Counting	We study video crowd counting, which is to estimate the number of objects (people in this paper) in all the frames of a video sequence. Previous work on crowd counting is mostly on still images. There has been little work on how to properly extract and take advantage of the spatial-temporal correlation between neighbor...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	232,660
2401.03197	Decision Making in Non-Stationary Environments with Policy-Augmented Search	Sequential decision-making under uncertainty is present in many important problems. Two popular approaches for tackling such problems are reinforcement learning and online search (e.g., Monte Carlo tree search). While the former learns a policy by interacting with the environment (typically done before execution), the ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	420,015
2201.08174	Knowledge Graph Question Answering Leaderboard: A Community Resource to Prevent a Replication Crisis	Data-driven systems need to be evaluated to establish trust in the scientific approach and its applicability. In particular, this is true for Knowledge Graph (KG) Question Answering (QA), where complex data structures are made accessible via natural-language interfaces. Evaluating the capabilities of these systems has ...	false	false	false	false	true	true	false	false	true	false	false	false	false	false	false	false	false	false	276,256
2101.11251	e-ACJ: Accurate Junction Extraction For Event Cameras	Junctions reflect the important geometrical structure information of the image, and are of primary significance to applications such as image matching and motion analysis. Previous event-based feature extraction methods are mainly focused on corners, which mainly find their locations, however, ignoring the geometrical ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	217,210
1404.7789	Phase transitions in semisupervised clustering of sparse networks	Predicting labels of nodes in a network, such as community memberships or demographic variables, is an important problem with applications in social and biological networks. A recently-discovered phase transition puts fundamental limits on the accuracy of these predictions if we have access only to the network topology...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	32,724
0904.0300	Design, development and implementation of a tool for construction of declarative functional descriptions of semantic web services based on WSMO methodology	Semantic web services (SWS) are self-contained, self-describing, semantically marked-up software resources that can be published, discovered, composed and executed across the Web in a semi-automatic way. They are a key component of the future Semantic Web, in which networked computer programs become providers and users...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	3,465
2410.03645	GenSim2: Scaling Robot Data Generation with Multi-modal and Reasoning LLMs	Robotic simulation today remains challenging to scale up due to the human efforts required to create diverse simulation tasks and scenes. Simulation-trained policies also face scalability issues as many sim-to-real methods focus on a single task. To address these challenges, this work proposes GenSim2, a scalable frame...	false	false	false	false	true	false	true	true	false	false	false	true	false	false	false	false	false	false	494,886
2211.10724	Deep Smart Contract Intent Detection	In recent years, research in software security has concentrated on identifying vulnerabilities in smart contracts to prevent significant losses of crypto assets on blockchains. Despite early successes in this area, detecting developers' intents in smart contracts has become a more pressing issue, as malicious intents h...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	331,415
2303.10056	GlueGen: Plug and Play Multi-modal Encoders for X-to-image Generation	Text-to-image (T2I) models based on diffusion processes have achieved remarkable success in controllable image generation using user-provided captions. However, the tight coupling between the current text encoder and image decoder in T2I models makes it challenging to replace or upgrade. Such changes often require mass...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	352,288
2104.13665	Robust Face-Swap Detection Based on 3D Facial Shape Information	Maliciously-manipulated images or videos - so-called deep fakes - especially face-swap images and videos have attracted more and more malicious attackers to discredit some key figures. Previous pixel-level artifacts based detection techniques always focus on some unclear patterns but ignore some available semantic clue...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	232,576
1801.04973	Two-Stage LASSO ADMM Signal Detection Algorithm For Large Scale MIMO	This paper explores the benefit of using some of the machine learning techniques and Big data optimization tools in approximating maximum likelihood (ML) detection of Large Scale MIMO systems. First, large scale MIMO detection problem is formulated as a LASSO (Least Absolute Shrinkage and Selection Operator) optimizati...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	88,368
1309.7697	Semi-structured data extraction and modelling: the WIA Project	Over the last decades, the amount of data of all kinds available electronically has increased dramatically. Data are accessible through a range of interfaces including Web browsers, database query languages, application-specific interfaces, built on top of a number of different data exchange formats. All these data spa...	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	true	false	true	27,413
1908.05902	MFA is a Waste of Time! Understanding Negative Connotation Towards MFA Applications via User Generated Content	Traditional single-factor authentication possesses several critical security vulnerabilities due to single-point failure feature. Multi-factor authentication (MFA), intends to enhance security by providing additional verification steps. However, in practical deployment, users often experience dissatisfaction while usin...	true	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	141,856
1610.03759	Language Models with Pre-Trained (GloVe) Word Embeddings	In this work we implement a training of a Language Model (LM), using Recurrent Neural Network (RNN) and GloVe word embeddings, introduced by Pennigton et al. in [1]. The implementation is following the general idea of training RNNs for LM tasks presented in [2], but is rather using Gated Recurrent Unit (GRU) [3] for a ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	62,295
2410.16451	Susu Box or Piggy Bank: Assessing Cultural Commonsense Knowledge between Ghana and the U.S	Recent work has highlighted the culturally-contingent nature of commonsense knowledge. We introduce AMAMMER${\epsilon}$, a test set of 525 multiple-choice questions designed to evaluate the commonsense knowledge of English LLMs, relative to the cultural contexts of Ghana and the United States. To create AMAMMER${\epsil...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	501,031
1211.2459	Measures of Entropy from Data Using Infinitely Divisible Kernels	Information theory provides principled ways to analyze different inference and learning problems such as hypothesis testing, clustering, dimensionality reduction, classification, among others. However, the use of information theoretic quantities as test statistics, that is, as quantities obtained from empirical data, p...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	19,680
2208.01453	Mitigating Smart Jammers in Multi-User MIMO	Wireless systems must be resilient to jamming attacks. Existing mitigation methods based on multi-antenna processing require knowledge of the jammer's transmit characteristics that may be difficult to acquire, especially for smart jammers that evade mitigation by transmitting only at specific instants. We propose a nov...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	311,170
2402.08126	Contextual Multinomial Logit Bandits with General Value Functions	Contextual multinomial logit (MNL) bandits capture many real-world assortment recommendation problems such as online retailing/advertising. However, prior work has only considered (generalized) linear value functions, which greatly limits its applicability. Motivated by this fact, in this work, we consider contextual M...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	428,971
1805.01947	Circuit designs for superconducting optoelectronic loop neurons	Optical communication achieves high fanout and short delay advantageous for information integration in neural systems. Superconducting detectors enable signaling with single photons for maximal energy efficiency. We present designs of superconducting optoelectronic neurons based on superconducting single-photon detecto...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	true	96,734
2006.06664	Quasi-Dense Similarity Learning for Multiple Object Tracking	Similarity learning has been recognized as a crucial step for object tracking. However, existing multiple object tracking methods only use sparse ground truth matching as the training objective, while ignoring the majority of the informative regions on the images. In this paper, we present Quasi-Dense Similarity Learni...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	181,515
2412.00139	EFSA: Episodic Few-Shot Adaptation for Text-to-Image Retrieval	Text-to-image retrieval is a critical task for managing diverse visual content, but common benchmarks for the task rely on small, single-domain datasets that fail to capture real-world complexity. Pre-trained vision-language models tend to perform well with easy negatives but struggle with hard negatives--visually simi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	512,526
2406.08488	ICE-G: Image Conditional Editing of 3D Gaussian Splats	Recently many techniques have emerged to create high quality 3D assets and scenes. When it comes to editing of these objects, however, existing approaches are either slow, compromise on quality, or do not provide enough customization. We introduce a novel approach to quickly edit a 3D model from a single reference view...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	463,517
2104.08623	Learning Fuzzy Clustering for SPECT/CT Segmentation via Convolutional Neural Networks	Quantitative bone single-photon emission computed tomography (QBSPECT) has the potential to provide a better quantitative assessment of bone metastasis than planar bone scintigraphy due to its ability to better quantify activity in overlapping structures. An important element of assessing response of bone metastasis is...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	230,879
1811.11728	Attributed Network Embedding for Incomplete Attributed Networks	Attributed networks are ubiquitous since a network often comes with auxiliary attribute information e.g. a social network with user profiles. Attributed Network Embedding (ANE) has recently attracted considerable attention, which aims to learn unified low dimensional node embeddings while preserving both structural and...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	114,842
2306.04498	Fair Multi-Agent Bandits	In this paper, we study the problem of fair multi-agent multi-arm bandit learning when agents do not communicate with each other, except collision information, provided to agents accessing the same arm simultaneously. We provide an algorithm with regret $O\left(N^3 \log \frac{B}{\Delta} f(\log T) \log T \right)$ (assum...	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	true	371,767

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