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
2005.11610	One-Shot Unsupervised Cross-Domain Detection	Despite impressive progress in object detection over the last years, it is still an open challenge to reliably detect objects across visual domains. Although the topic has attracted attention recently, current approaches all rely on the ability to access a sizable amount of target data for use at training time. This is...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	178,512
0803.1586	Spatio-activity based object detection	We present the SAMMI lightweight object detection method which has a high level of accuracy and robustness, and which is able to operate in an environment with a large number of cameras. Background modeling is based on DCT coefficients provided by cameras. Foreground detection uses similarity in temporal characteristic...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	1,420
1710.02316	A Multiscale Patch Based Convolutional Network for Brain Tumor Segmentation	This article presents a multiscale patch based convolutional neural network for the automatic segmentation of brain tumors in multi-modality 3D MR images. We use multiscale deep supervision and inputs to train a convolutional network. We evaluate the effectiveness of the proposed approach on the BRATS 2017 segmentation...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	82,158
2106.05006	Text-to-SQL in the Wild: A Naturally-Occurring Dataset Based on Stack Exchange Data	Most available semantic parsing datasets, comprising of pairs of natural utterances and logical forms, were collected solely for the purpose of training and evaluation of natural language understanding systems. As a result, they do not contain any of the richness and variety of natural-occurring utterances, where human...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	239,939
2408.09106	Fragment-Masked Molecular Optimization	Molecular optimization is a crucial aspect of drug discovery, aimed at refining molecular structures to enhance drug efficacy and minimize side effects, ultimately accelerating the overall drug development process. Many target-based molecular optimization methods have been proposed, significantly advancing drug discove...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	481,276
2412.14077	Dialogue with the Machine and Dialogue with the Art World: Evaluating Generative AI for Culturally-Situated Creativity	This paper proposes dialogue as a method for evaluating generative AI tools for culturally-situated creative practice, that recognizes the socially situated nature of art. Drawing on sociologist Howard Becker's concept of Art Worlds, this method expands the scope of traditional AI and creativity evaluations beyond benc...	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	518,563
2207.00837	UTD-Yolov5: A Real-time Underwater Targets Detection Method based on Attention Improved YOLOv5	As the treasure house of nature, the ocean contains abundant resources. But the coral reefs, which are crucial to the sustainable development of marine life, are facing a huge crisis because of the existence of COTS and other organisms. The protection of society through manual labor is limited and inefficient. The unpr...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	305,924
2006.11747	Weak Supervision and Referring Attention for Temporal-Textual Association Learning	A system capturing the association between video frames and textual queries offer great potential for better video analysis. However, training such a system in a fully supervised way inevitably demands a meticulously curated video dataset with temporal-textual annotations. Therefore we provide a Weak-Supervised alterna...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	183,359
2107.12421	Parallel Surrogate-assisted Optimization Using Mesh Adaptive Direct Search	We consider computationally expensive blackbox optimization problems and present a method that employs surrogate models and concurrent computing at the search step of the mesh adaptive direct search (MADS) algorithm. Specifically, we solve a surrogate optimization problem using locally weighted scatterplot smoothing (L...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	247,881
2203.08161	Sensitivity Estimation for Dark Matter Subhalos in Synthetic Gaia DR2 using Deep Learning	The abundance of dark matter (DM) subhalos orbiting a host galaxy is a generic prediction of the cosmological framework, and is a promising way to constrain the nature of DM. In this paper, we investigate the use of machine learning-based tools to quantify the magnitude of phase-space perturbations caused by the passag...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	285,697
2406.07590	StreamFP: Learnable Fingerprint-guided Data Selection for Efficient Stream Learning	Stream Learning (SL) requires models that can quickly adapt to continuously evolving data, posing significant challenges in both computational efficiency and learning accuracy. Effective data selection is critical in SL to ensure a balance between information retention and training efficiency. Traditional rule-based da...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	463,130
2205.00638	A Two Parameters Equation for Word Rank-Frequency Relation	Let $f (\cdot)$ be the absolute frequency of words and $r$ be the rank of words in decreasing order of frequency, then the following function can fit the rank-frequency relation \[ f (r;s,t) = \left(\frac{r_{\tt max}}{r}\right)^{1-s} \left(\frac{r_{\tt max}+t \cdot r_{\tt exp}}{r+t \cdot r_{\tt exp}}\right)^{1+(1+t)s} ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	294,331
2003.10212	An Improved EEG Acquisition Protocol Facilitates Localized Neural Activation	This work proposes improvements in the electroencephalogram (EEG) recording protocols for motor imagery through the introduction of actual motor movement and/or somatosensory cues. The results obtained demonstrate the advantage of requiring the subjects to perform motor actions following the trials of imagery. By intro...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	169,271
2112.05353	Learning-Augmented Algorithms for Online Steiner Tree	This paper considers the recently popular beyond-worst-case algorithm analysis model which integrates machine-learned predictions with online algorithm design. We consider the online Steiner tree problem in this model for both directed and undirected graphs. Steiner tree is known to have strong lower bounds in the onli...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	270,817
1409.4575	Stable Cosparse Recovery via \ell_p-analysis Optimization	In this paper we study the $\ell_p$-analysis optimization ($0<p\leq1$) problem for cosparse signal recovery. We establish a bound for recovery error via the restricted $p$-isometry property over any subspace. We further prove that the nonconvex $\ell_q$-analysis optimization can do recovery with a lower sample complexi...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	36,087
1705.03760	Uplink Analysis of Large MU-MIMO Systems With Space-Constrained Arrays in Ricean Fading	Closed-form approximations to the expected per-terminal signal-to-interference-plus-noise-ratio (SINR) and ergodic sum spectral efficiency of a large multiuser multiple-input multiple-output system are presented. Our analysis assumes correlated Ricean fading with maximum ratio combining on the uplink, where the base st...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	73,230
2403.04937	Gradient-free neural topology optimization: Towards effective fracture-resistant designs	Gradient-free optimizers allow for tackling problems regardless of the smoothness or differentiability of their objective function, but they require many more iterations to converge when compared to gradient-based algorithms. This has made them unviable for topology optimization due to the high computational cost per i...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	435,796
1302.3126	Is Europe Evolving Toward an Integrated Research Area?	An integrated European Research Area (ERA) is a critical component for a more competitive and open European R&D system. However, the impact of EU-specific integration policies aimed at overcoming innovation barriers associated with national borders is not well understood. Here we analyze 2.4 x 10^6 patent applications ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	21,990
1405.1535	Learning Boolean Halfspaces with Small Weights from Membership Queries	We consider the problem of proper learning a Boolean Halfspace with integer weights $\{0,1,\ldots,t\}$ from membership queries only. The best known algorithm for this problem is an adaptive algorithm that asks $n^{O(t^5)}$ membership queries where the best lower bound for the number of membership queries is $n^t$ [Lear...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	32,894
2406.19323	Multimodal Visual-haptic pose estimation in the presence of transient occlusion	Human-robot collaboration requires the establishment of methods to guarantee the safety of participating operators. A necessary part of this process is ensuring reliable human pose estimation. Established vision-based modalities encounter problems when under conditions of occlusion. This article describes the combinati...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	468,381
1608.04862	Feature Driven and Point Process Approaches for Popularity Prediction	Predicting popularity, or the total volume of information outbreaks, is an important subproblem for understanding collective behavior in networks. Each of the two main types of recent approaches to the problem, feature-driven and generative models, have desired qualities and clear limitations. This paper bridges the ga...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	59,895
2006.12282	Cost Restrained Hybrid Attacks in Power Grids	The frequent occurrences of cascading failures in power grids have been receiving continuous attention in recent years. An urgent task for us is to understand the cascading failure vulnerability of power grids against various kinds of attacks. We consider a cost restrained hybrid attack problem in power grids, in which...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	183,535
2106.15493	Generalized Orthogonal Procrustes Problem under Arbitrary Adversaries	The generalized orthogonal Procrustes problem (GOPP) plays a fundamental role in several scientific disciplines including statistics, imaging science and computer vision. Despite its tremendous practical importance, it is generally an NP-hard problem to find the least squares estimator. We study the semidefinite relaxa...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	243,783
2410.10347	A Unified Approach to Routing and Cascading for LLMs	The availability of a wide range of large language models (LLMs) embedded in various agentic systems has significantly increased the potential of model selection strategies to improve the cost-performance tradeoff. Existing strategies involve either routing, where a single model is chosen per query, or cascading, which...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	498,028
2209.11299	Deep Domain Adaptation for Detecting Bomb Craters in Aerial Images	The aftermath of air raids can still be seen for decades after the devastating events. Unexploded ordnance (UXO) is an immense danger to human life and the environment. Through the assessment of wartime images, experts can infer the occurrence of a dud. The current manual analysis process is expensive and time-consumin...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	319,138
1602.03436	High-Dimensional Estimation of Structured Signals from Non-Linear Observations with General Convex Loss Functions	In this paper, we study the issue of estimating a structured signal $x_0 \in \mathbb{R}^n$ from non-linear and noisy Gaussian observations. Supposing that $x_0$ is contained in a certain convex subset $K \subset \mathbb{R}^n$, we prove that accurate recovery is already feasible if the number of observations exceeds the...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	51,998
2006.12228	Differentiable PAC-Bayes Objectives with Partially Aggregated Neural Networks	We make three related contributions motivated by the challenge of training stochastic neural networks, particularly in a PAC-Bayesian setting: (1) we show how averaging over an ensemble of stochastic neural networks enables a new class of \emph{partially-aggregated} estimators; (2) we show that these lead to provably l...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	183,517
1404.2353	Power System Parameters Forecasting Using Hilbert-Huang Transform and Machine Learning	A novel hybrid data-driven approach is developed for forecasting power system parameters with the goal of increasing the efficiency of short-term forecasting studies for non-stationary time-series. The proposed approach is based on mode decomposition and a feature analysis of initial retrospective data using the Hilber...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	32,208
2406.06372	Improving Deep Learning-based Automatic Cranial Defect Reconstruction by Heavy Data Augmentation: From Image Registration to Latent Diffusion Models	Modeling and manufacturing of personalized cranial implants are important research areas that may decrease the waiting time for patients suffering from cranial damage. The modeling of personalized implants may be partially automated by the use of deep learning-based methods. However, this task suffers from difficulties...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	462,543
1611.04583	Degree Distribution, Rank-size Distribution, and Leadership Persistence in Mediation-Driven Attachment Networks	We investigate the growth of a class of networks in which a new node first picks a mediator at random and connects with $m$ randomly chosen neighbors of the mediator at each time step. We show that degree distribution in such a mediation-driven attachment (MDA) network exhibits power-law $P(k)\sim k^{-\gamma(m)}$ with ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	63,872
2404.12386	SOHES: Self-supervised Open-world Hierarchical Entity Segmentation	Open-world entity segmentation, as an emerging computer vision task, aims at segmenting entities in images without being restricted by pre-defined classes, offering impressive generalization capabilities on unseen images and concepts. Despite its promise, existing entity segmentation methods like Segment Anything Model...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	447,865
2003.00269	Online Binary Space Partitioning Forests	The Binary Space Partitioning-Tree~(BSP-Tree) process was recently proposed as an efficient strategy for space partitioning tasks. Because it uses more than one dimension to partition the space, the BSP-Tree Process is more efficient and flexible than conventional axis-aligned cutting strategies. However, due to its ba...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	166,247
1812.10885	Coarse-to-fine Semantic Segmentation from Image-level Labels	Deep neural network-based semantic segmentation generally requires large-scale cost extensive annotations for training to obtain better performance. To avoid pixel-wise segmentation annotations which are needed for most methods, recently some researchers attempted to use object-level labels (e.g. bounding boxes) or ima...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	117,456
2408.08799	Representation Learning of Geometric Trees	Geometric trees are characterized by their tree-structured layout and spatially constrained nodes and edges, which significantly impacts their topological attributes. This inherent hierarchical structure plays a crucial role in domains such as neuron morphology and river geomorphology, but traditional graph representat...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	481,156
1811.02546	A Model for General Intelligence	The overarching problem in artificial intelligence (AI) is that we do not understand the intelligence process well enough to enable the development of adequate computational models. Much work has been done in AI over the years at lower levels, but a big part of what has been missing involves the high level, abstract, g...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	112,613
2303.08308	SpaceEvo: Hardware-Friendly Search Space Design for Efficient INT8 Inference	The combination of Neural Architecture Search (NAS) and quantization has proven successful in automatically designing low-FLOPs INT8 quantized neural networks (QNN). However, directly applying NAS to design accurate QNN models that achieve low latency on real-world devices leads to inferior performance. In this work, w...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	351,590
1911.08654	Adversarial Robustness of Flow-Based Generative Models	Flow-based generative models leverage invertible generator functions to fit a distribution to the training data using maximum likelihood. Despite their use in several application domains, robustness of these models to adversarial attacks has hardly been explored. In this paper, we study adversarial robustness of flow-b...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	154,256
1807.06306	Joint Power and Time Allocation for NOMA-MEC Offloading	This paper considers non-orthogonal multiple access (NOMA) assisted mobile edge computing (MEC), where the power and time allocation is jointly optimized to reduce the energy consumption of offloading. Closed-form expressions for the optimal power and time allocation solutions are obtained and used to establish the con...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	103,101
1312.7366	Monte Carlo non local means: Random sampling for large-scale image filtering	We propose a randomized version of the non-local means (NLM) algorithm for large-scale image filtering. The new algorithm, called Monte Carlo non-local means (MCNLM), speeds up the classical NLM by computing a small subset of image patch distances, which are randomly selected according to a designed sampling pattern. W...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	29,469
2106.14989	Object Detection Based Handwriting Localization	We present an object detection based approach to localize handwritten regions from documents, which initially aims to enhance the anonymization during the data transmission. The concatenated fusion of original and preprocessed images containing both printed texts and handwritten notes or signatures are fed into the con...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	243,571
1309.1501	Improvements to deep convolutional neural networks for LVCSR	Deep Convolutional Neural Networks (CNNs) are more powerful than Deep Neural Networks (DNN), as they are able to better reduce spectral variation in the input signal. This has also been confirmed experimentally, with CNNs showing improvements in word error rate (WER) between 4-12% relative compared to DNNs across a var...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	true	false	false	26,863
1502.07809	Optimal Energy-Efficient Regular Delivery of Packets in Cyber-Physical Systems	In cyber-physical systems such as in-vehicle wireless sensor networks, a large number of sensor nodes continually generate measurements that should be received by other nodes such as actuators in a regular fashion. Meanwhile, energy-efficiency is also important in wireless sensor networks. Motivated by these, we develo...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	40,615
1906.07040	Making Fast Graph-based Algorithms with Graph Metric Embeddings	The computation of distance measures between nodes in graphs is inefficient and does not scale to large graphs. We explore dense vector representations as an effective way to approximate the same information: we introduce a simple yet efficient and effective approach for learning graph embeddings. Instead of directly o...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	135,496
2302.00514	Towards Implementing Energy-aware Data-driven Intelligence for Smart Health Applications on Mobile Platforms	Recent breakthrough technological progressions of powerful mobile computing resources such as low-cost mobile GPUs along with cutting-edge, open-source software architectures have enabled high-performance deep learning on mobile platforms. These advancements have revolutionized the capabilities of today's mobile applic...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	343,240
2302.03226	Vibration-suppressed toolpath generation for kinematic and energy performance optimization in large dimension additive manufacturing	Product quality and sustainability in large dimension additive manufacturing (LDAM) highly rely on the kinematic and energy performance of LDAM systems. However, the mechanical vibration occurring in the fabrication process hinders the further development of AM technology in industrial manufacturing. To this end, a kin...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	344,263
2010.08397	Few-shot model-based adaptation in noisy conditions	Few-shot adaptation is a challenging problem in the context of simulation-to-real transfer in robotics, requiring safe and informative data collection. In physical systems, additional challenge may be posed by domain noise, which is present in virtually all real-world applications. In this paper, we propose to perform ...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	201,168
1711.03953	Breaking the Softmax Bottleneck: A High-Rank RNN Language Model	We formulate language modeling as a matrix factorization problem, and show that the expressiveness of Softmax-based models (including the majority of neural language models) is limited by a Softmax bottleneck. Given that natural language is highly context-dependent, this further implies that in practice Softmax with di...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	84,300
1301.4938	A type theoretical framework for natural language semantics: the Montagovian generative lexicon	We present a framework, named the Montagovian generative lexicon, for computing the semantics of natural language sentences, expressed in many sorted higher order logic. Word meaning is depicted by lambda terms of second order lambda calculus (Girard's system F) with base types including a type for propositions and man...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	21,294
2004.05054	ASL Recognition with Metric-Learning based Lightweight Network	In the past decades the set of human tasks that are solved by machines was extended dramatically. From simple image classification problems researchers now move towards solving more sophisticated and vital problems, like, autonomous driving and language translation. The case of language translation includes a challengi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	172,076
1406.6453	A Quantitative Neural Coding Model of Sensory Memory	The coding mechanism of sensory memory on the neuron scale is one of the most important questions in neuroscience. We have put forward a quantitative neural network model, which is self organized, self similar, and self adaptive, just like an ecosystem following Darwin theory. According to this model, neural coding is ...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	34,120
2311.16766	Rescuing referral failures during automated diagnosis of domain-shifted medical images	The success of deep learning models deployed in the real world depends critically on their ability to generalize well across diverse data domains. Here, we address a fundamental challenge with selective classification during automated diagnosis with domain-shifted medical images. In this scenario, models must learn to ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	411,038
1303.0699	Low-complexity dominance-based Sphere Decoder for MIMO Systems	The sphere decoder (SD) is an attractive low-complexity alternative to maximum likelihood (ML) detection in a variety of communication systems. It is also employed in multiple-input multiple-output (MIMO) systems where the computational complexity of the optimum detector grows exponentially with the number of transmit ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	22,612
2310.06622	Robustness May be More Brittle than We Think under Different Degrees of Distribution Shifts	Out-of-distribution (OOD) generalization is a complicated problem due to the idiosyncrasies of possible distribution shifts between training and test domains. Most benchmarks employ diverse datasets to address this issue; however, the degree of the distribution shift between the training domains and the test domains of...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	398,649
2212.11261	Contrastive Language-Vision AI Models Pretrained on Web-Scraped Multimodal Data Exhibit Sexual Objectification Bias	Nine language-vision AI models trained on web scrapes with the Contrastive Language-Image Pretraining (CLIP) objective are evaluated for evidence of a bias studied by psychologists: the sexual objectification of girls and women, which occurs when a person's human characteristics, such as emotions, are disregarded and t...	false	false	false	false	true	false	true	false	true	false	false	true	false	true	false	false	false	false	337,740
2405.09601	Fully Automated OCT-based Tissue Screening System	This study introduces a groundbreaking optical coherence tomography (OCT) imaging system dedicated for high-throughput screening applications using ex vivo tissue culture. Leveraging OCT's non-invasive, high-resolution capabilities, the system is equipped with a custom-designed motorized platform and tissue detection a...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	454,480
2207.02201	Efficient Spatial-Temporal Information Fusion for LiDAR-Based 3D Moving Object Segmentation	Accurate moving object segmentation is an essential task for autonomous driving. It can provide effective information for many downstream tasks, such as collision avoidance, path planning, and static map construction. How to effectively exploit the spatial-temporal information is a critical question for 3D LiDAR moving...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	306,438
2404.09595	Building Semantic Communication System via Molecules: An End-to-End Training Approach	The concept of semantic communication provides a novel approach for applications in scenarios with limited communication resources. In this paper, we propose an end-to-end (E2E) semantic molecular communication system, aiming to enhance the efficiency of molecular communication systems by reducing the transmitted infor...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	446,753
2412.19750	IMAGINE: An 8-to-1b 22nm FD-SOI Compute-In-Memory CNN Accelerator With an End-to-End Analog Charge-Based 0.15-8POPS/W Macro Featuring Distribution-Aware Data Reshaping	Charge-domain compute-in-memory (CIM) SRAMs have recently become an enticing compromise between computing efficiency and accuracy to process sub-8b convolutional neural networks (CNNs) at the edge. Yet, they commonly make use of a fixed dot-product (DP) voltage swing, which leads to a loss in effective ADC bits due to ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	520,954
2012.08723	Exacerbating Algorithmic Bias through Fairness Attacks	Algorithmic fairness has attracted significant attention in recent years, with many quantitative measures suggested for characterizing the fairness of different machine learning algorithms. Despite this interest, the robustness of those fairness measures with respect to an intentional adversarial attack has not been pr...	false	false	false	false	true	false	true	false	false	false	false	false	true	false	false	false	false	false	211,846
2204.09030	Decentralized Control of Distributed Cloud Networks with Generalized Network Flows	Emerging distributed cloud architectures, e.g., fog and mobile edge computing, are playing an increasingly important role in the efficient delivery of real-time stream-processing applications (also referred to as augmented information services), such as industrial automation and metaverse experiences (e.g., extended re...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	292,290
2309.09013	Bridging Dense and Sparse Maximum Inner Product Search	Maximum inner product search (MIPS) over dense and sparse vectors have progressed independently in a bifurcated literature for decades; the latter is better known as top-$k$ retrieval in Information Retrieval. This duality exists because sparse and dense vectors serve different end goals. That is despite the fact that ...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	392,435
1511.09196	Non-adaptive Group Testing on Graphs	Grebinski and Kucherov (1998) and Alon et al. (2004-2005) study the problem of learning a hidden graph for some especial cases, such as hamiltonian cycle, cliques, stars, and matchings. This problem is motivated by problems in chemical reactions, molecular biology and genome sequencing. In this paper, we present a ge...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	49,645
2201.01426	Advancing 3D Medical Image Analysis with Variable Dimension Transform based Supervised 3D Pre-training	The difficulties in both data acquisition and annotation substantially restrict the sample sizes of training datasets for 3D medical imaging applications. As a result, constructing high-performance 3D convolutional neural networks from scratch remains a difficult task in the absence of a sufficient pre-training paramet...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	274,249
2307.12229	EchoGLAD: Hierarchical Graph Neural Networks for Left Ventricle Landmark Detection on Echocardiograms	The functional assessment of the left ventricle chamber of the heart requires detecting four landmark locations and measuring the internal dimension of the left ventricle and the approximate mass of the surrounding muscle. The key challenge of automating this task with machine learning is the sparsity of clinical label...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	381,189
1810.05729	UOLO - automatic object detection and segmentation in biomedical images	We propose UOLO, a novel framework for the simultaneous detection and segmentation of structures of interest in medical images. UOLO consists of an object segmentation module which intermediate abstract representations are processed and used as input for object detection. The resulting system is optimized simultaneousl...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	110,295
1705.03881	Net2Vec: Deep Learning for the Network	We present Net2Vec, a flexible high-performance platform that allows the execution of deep learning algorithms in the communication network. Net2Vec is able to capture data from the network at more than 60Gbps, transform it into meaningful tuples and apply predictions over the tuples in real time. This platform can be ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	73,247
2107.02919	Distributed stochastic optimization with large delays	One of the most widely used methods for solving large-scale stochastic optimization problems is distributed asynchronous stochastic gradient descent (DASGD), a family of algorithms that result from parallelizing stochastic gradient descent on distributed computing architectures (possibly) asychronously. However, a key ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	244,985
2201.10848	Comparison of Depth Estimation Setups from Stereo Endoscopy and Optical Tracking for Point Measurements	To support minimally-invasive intraoperative mitral valve repair, quantitative measurements from the valve can be obtained using an infra-red tracked stylus. It is desirable to view such manually measured points together with the endoscopic image for further assistance. Therefore, hand-eye calibration is required that ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	277,119
2410.01367	Towards Dynamic Graph Neural Networks with Provably High-Order Expressive Power	Dynamic Graph Neural Networks (DyGNNs) have garnered increasing research attention for learning representations on evolving graphs. Despite their effectiveness, the limited expressive power of existing DyGNNs hinders them from capturing important evolving patterns of dynamic graphs. Although some works attempt to enhan...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	493,738
1802.04425	"How Was Your Weekend?" A Generative Model of Phatic Conversation	Unspoken social rules, such as those that govern choosing a proper discussion topic and when to change discussion topics, guide conversational behaviors. We propose a computational model of conversation that can follow or break such rules, with participant agents that respond accordingly. Additionally, we demonstrate a...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	90,217
2206.05542	Surround-View Cameras based Holistic Visual Perception for Automated Driving	The formation of eyes led to the big bang of evolution. The dynamics changed from a primitive organism waiting for the food to come into contact for eating food being sought after by visual sensors. The human eye is one of the most sophisticated developments of evolution, but it still has defects. Humans have evolved a...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	302,043
2403.02372	OTClean: Data Cleaning for Conditional Independence Violations using Optimal Transport	Ensuring Conditional Independence (CI) constraints is pivotal for the development of fair and trustworthy machine learning models. In this paper, we introduce \sys, a framework that harnesses optimal transport theory for data repair under CI constraints. Optimal transport theory provides a rigorous framework for measur...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	true	false	434,789
1707.06772	Improved Bilinear Pooling with CNNs	Bilinear pooling of Convolutional Neural Network (CNN) features [22, 23], and their compact variants [10], have been shown to be effective at fine-grained recognition, scene categorization, texture recognition, and visual question-answering tasks among others. The resulting representation captures second-order statisti...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	77,481
2406.07284	Unsupervised Object Detection with Theoretical Guarantees	Unsupervised object detection using deep neural networks is typically a difficult problem with few to no guarantees about the learned representation. In this work we present the first unsupervised object detection method that is theoretically guaranteed to recover the true object positions up to quantifiable small shif...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	462,980
1910.01191	Adaptive Generation of Phantom Limbs Using Visible Hierarchical Autoencoders	This paper proposed a hierarchical visible autoencoder in the adaptive phantom limbs generation according to the kinetic behavior of functional body-parts, which are measured by heterogeneous kinetic sensors. The proposed visible hierarchical autoencoder consists of interpretable and multi-correlated autoencoder pipeli...	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	147,863
2405.00394	Enhancing Mutual Trustworthiness in Federated Learning for Data-Rich Smart Cities	Federated learning is a promising collaborative and privacy-preserving machine learning approach in data-rich smart cities. Nevertheless, the inherent heterogeneity of these urban environments presents a significant challenge in selecting trustworthy clients for collaborative model training. The usage of traditional ap...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	450,906
2205.08220	Rate-Region Characterization and Channel Estimation for Cell-Free Symbiotic Radio Communications	Cell-free massive MIMO and symbiotic radio communication have been recently proposed as the promising beyond fifth-generation (B5G) networking architecture and transmission technology, respectively. To reap the benefits of both, this paper studies cell-free symbiotic radio communication systems, where a number of cell-...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	296,857
2312.12717	DoDo-Code: a Deep Levenshtein Distance Embedding-based Code for IDS Channel and DNA Storage	Recently, DNA storage has emerged as a promising data storage solution, offering significant advantages in storage density, maintenance cost efficiency, and parallel replication capability. Mathematically, the DNA storage pipeline can be viewed as an insertion, deletion, and substitution (IDS) channel. Because of the m...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	417,069
1808.02560	Belief likelihood function for generalised logistic regression	The notion of belief likelihood function of repeated trials is introduced, whenever the uncertainty for individual trials is encoded by a belief measure (a finite random set). This generalises the traditional likelihood function, and provides a natural setting for belief inference from statistical data. Factorisation r...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	104,794
2002.07236	GACEM: Generalized Autoregressive Cross Entropy Method for Multi-Modal Black Box Constraint Satisfaction	In this work we present a new method of black-box optimization and constraint satisfaction. Existing algorithms that have attempted to solve this problem are unable to consider multiple modes, and are not able to adapt to changes in environment dynamics. To address these issues, we developed a modified Cross-Entropy Me...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	164,408
1309.7524	Meme and Variations: A Computer Model of Cultural Evolution	Holland's (1975) genetic algorithm is a minimal computer model of natural selection that made it possible to investigate the effect of manipulating specific parameters on the evolutionary process. If culture is, like biology, a form of evolution, it should be possible to similarly abstract the underlying skeleton of th...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	false	false	27,386
1010.5720	Information-theoretic inference of common ancestors	A directed acyclic graph (DAG) partially represents the conditional independence structure among observations of a system if the local Markov condition holds, that is, if every variable is independent of its non-descendants given its parents. In general, there is a whole class of DAGs that represents a given set of con...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	8,048
2107.01105	Memory Efficient Meta-Learning with Large Images	Meta learning approaches to few-shot classification are computationally efficient at test time, requiring just a few optimization steps or single forward pass to learn a new task, but they remain highly memory-intensive to train. This limitation arises because a task's entire support set, which can contain up to 1000 i...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	244,372
2409.11006	Uncertainty Analysis of Limit Cycle Oscillations in Nonlinear Dynamical Systems with the Fourier Generalized Polynomial Chaos Expansion	In engineering, simulations play a vital role in predicting the behavior of a nonlinear dynamical system. In order to enhance the reliability of predictions, it is essential to incorporate the inherent uncertainties that are present in all real-world systems. Consequently, stochastic predictions are of significant impo...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	488,973
2404.17395	Situational Graphs for Robotic First Responders: an application to dismantling drug labs	In this work, we support experts in the safety domain with safer dismantling of drug labs, by deploying robots for the initial inspection. Being able to act on the discovered environment is key to enabling this (semi-)autonomous inspection, e.g. to open doors or take a closer at suspicious items. Our approach addresses...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	449,844
1905.00336	Bean Split Ratio for Dry Bean Canning Quality and Variety Analysis	Splits on canned beans appear in the process of preparation and canning. Researchers are studying how they are influenced by cooking environment and genotype. However, there is no existing method to automatically quantify or to characterize the severity of splits. To solve this, we propose two measures: the Bean Split ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	129,447
2411.12011	SynCoTrain: A Dual Classifier PU-learning Framework for Synthesizability Prediction	Material discovery is a cornerstone of modern science, driving advancements in diverse disciplines from biomedical technology to climate solutions. Predicting synthesizability, a critical factor in realizing novel materials, remains a complex challenge due to the limitations of traditional heuristics and thermodynamic ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	509,261
2006.08655	Variational Bayesian Monte Carlo with Noisy Likelihoods	Variational Bayesian Monte Carlo (VBMC) is a recently introduced framework that uses Gaussian process surrogates to perform approximate Bayesian inference in models with black-box, non-cheap likelihoods. In this work, we extend VBMC to deal with noisy log-likelihood evaluations, such as those arising from simulation-ba...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	182,248
1504.04588	The Nataf-Beta Random Field Classifier: An Extension of the Beta Conjugate Prior to Classification Problems	This paper presents the Nataf-Beta Random Field Classifier, a discriminative approach that extends the applicability of the Beta conjugate prior to classification problems. The approach's key feature is to model the probability of a class conditional on attribute values as a random field whose marginals are Beta distri...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	42,154
1903.08806	Robust Contraction Analysis of Nonlinear Systems via Differential IQC	We present a new approach to verifying contraction and $L_2$-gain of uncertain nonlinear systems, extending the well-known method of integral quadratic constraints. The uncertain system consists of a feedback interconnection of a nonlinear nominal system and uncertainties satisfying differential integral quadratic cons...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	124,908
2201.09302	1000x Faster Camera and Machine Vision with Ordinary Devices	In digital cameras, we find a major limitation: the image and video form inherited from a film camera obstructs it from capturing the rapidly changing photonic world. Here, we present vidar, a bit sequence array where each bit represents whether the accumulation of photons has reached a threshold, to record and reconst...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	276,625
1611.02496	Multidimensional Asymptotic Consensus in Dynamic Networks	We study the problem of asymptotic consensus as it occurs in a wide range of applications in both man-made and natural systems. In particular, we study systems with directed communication graphs that may change over time. We recently proposed a new family of convex combination algorithms in dimension one whose weight...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	63,571
0906.1565	Correcting a Fraction of Errors in Nonbinary Expander Codes with Linear Programming	A linear-programming decoder for \emph{nonbinary} expander codes is presented. It is shown that the proposed decoder has the maximum-likelihood certificate properties. It is also shown that this decoder corrects any pattern of errors of a relative weight up to approximately 1/4 \delta_A \delta_B (where \delta_A and \de...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	3,846
2310.03036	A quantum system control method based on enhanced reinforcement learning	Traditional quantum system control methods often face different constraints, and are easy to cause both leakage and stochastic control errors under the condition of limited resources. Reinforcement learning has been proved as an efficient way to complete the quantum system control task. To learn a satisfactory control ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	397,110
1604.08859	The Z-loss: a shift and scale invariant classification loss belonging to the Spherical Family	Despite being the standard loss function to train multi-class neural networks, the log-softmax has two potential limitations. First, it involves computations that scale linearly with the number of output classes, which can restrict the size of problems we are able to tackle with current hardware. Second, it remains unc...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	55,267
2001.04830	Sequential Recommender Systems: Challenges, Progress and Prospects	The emerging topic of sequential recommender systems has attracted increasing attention in recent years.Different from the conventional recommender systems including collaborative filtering and content-based filtering, SRSs try to understand and model the sequential user behaviors, the interactions between users and it...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	160,373
2405.07962	KG-Planner: Knowledge-Informed Graph Neural Planning for Collaborative Manipulators	This paper presents a novel knowledge-informed graph neural planner (KG-Planner) to address the challenge of efficiently planning collision-free motions for robots in high-dimensional spaces, considering both static and dynamic environments involving humans. Unlike traditional motion planners that struggle with finding...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	453,928
1207.6563	Hidden information and regularities of information dynamics IIR	Part 1 has studied the conversion of observed random process with its hidden information to related dynamic process, applying entropy functional measure (EF) of the random process and path functional information measure (IPF) of the dynamic conversion process. The variation principle, satisfying the EF-IPF equivalence ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	17,798
cs/0311028	Using Counterfactuals in Knowledge-Based Programming	This paper adds counterfactuals to the framework of knowledge-based programs of Fagin, Halpern, Moses, and Vardi. The use of counterfactuals is illustrated by designing a protocol in which an agent stops sending messages once it knows that it is safe to do so. Such behavior is difficult to capture in the original frame...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	538,033
2103.07714	Mean Field Behaviour of Collaborative Multi-Agent Foragers	Collaborative multi-agent robotic systems where agents coordinate by modifying a shared environment often result in undesired dynamical couplings that complicate the analysis and experiments when solving a specific problem or task. Simultaneously, biologically-inspired robotics rely on simplifying agents and increasing...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	true	false	false	false	224,673

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