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
2307.12793	Imperfect CSI: A Key Factor of Uncertainty to Over-the-Air Federated Learning	Over-the-air computation (AirComp) has recently been identified as a prominent technique to enhance communication efficiency of wireless federated learning (FL). This letter investigates the impact of channel state information (CSI) uncertainty at the transmitter on an AirComp enabled FL (AirFL) system with the truncat...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	381,382
2501.17273	Tailored Truths: Optimizing LLM Persuasion with Personalization and Fabricated Statistics	Large Language Models (LLMs) are becoming increasingly persuasive, demonstrating the ability to personalize arguments in conversation with humans by leveraging their personal data. This may have serious impacts on the scale and effectiveness of disinformation campaigns. We studied the persuasiveness of LLMs in a debate...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	528,269
2212.10649	Inversion of Bayesian Networks	Variational autoencoders and Helmholtz machines use a recognition network (encoder) to approximate the posterior distribution of a generative model (decoder). In this paper we study the necessary and sufficient properties of a recognition network so that it can model the true posterior distribution exactly. These resul...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	337,547
2312.04234	Graph Convolutions Enrich the Self-Attention in Transformers!	Transformers, renowned for their self-attention mechanism, have achieved state-of-the-art performance across various tasks in natural language processing, computer vision, time-series modeling, etc. However, one of the challenges with deep Transformer models is the oversmoothing problem, where representations across la...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	413,606
1601.01121	A pragmatic approach to multi-class classification	We present a novel hierarchical approach to multi-class classification which is generic in that it can be applied to different classification models (e.g., support vector machines, perceptrons), and makes no explicit assumptions about the probabilistic structure of the problem as it is usually done in multi-class class...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	50,713
2410.06905	Reliable Probabilistic Human Trajectory Prediction for Autonomous Applications	Autonomous systems, like vehicles or robots, require reliable, accurate, fast, resource-efficient, scalable, and low-latency trajectory predictions to get initial knowledge about future locations and movements of surrounding objects for safe human-machine interaction. Furthermore, they need to know the uncertainty of t...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	496,389
2201.12701	DearFSAC: An Approach to Optimizing Unreliable Federated Learning via Deep Reinforcement Learning	In federated learning (FL), model aggregation has been widely adopted for data privacy. In recent years, assigning different weights to local models has been used to alleviate the FL performance degradation caused by differences between local datasets. However, when various defects make the FL process unreliable, most ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	277,749
2010.12729	ANLIzing the Adversarial Natural Language Inference Dataset	We perform an in-depth error analysis of Adversarial NLI (ANLI), a recently introduced large-scale human-and-model-in-the-loop natural language inference dataset collected over multiple rounds. We propose a fine-grained annotation scheme of the different aspects of inference that are responsible for the gold classifica...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	202,821
2312.02420	Towards Granularity-adjusted Pixel-level Semantic Annotation	Recent advancements in computer vision predominantly rely on learning-based systems, leveraging annotations as the driving force to develop specialized models. However, annotating pixel-level information, particularly in semantic segmentation, presents a challenging and labor-intensive task, prompting the need for auto...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	412,857
2205.07736	Prioritizing Corners in OoD Detectors via Symbolic String Manipulation	For safety assurance of deep neural networks (DNNs), out-of-distribution (OoD) monitoring techniques are essential as they filter spurious input that is distant from the training dataset. This paper studies the problem of systematically testing OoD monitors to avoid cases where an input data point is tested as in-distr...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	296,694
2311.05146	OW-SLR: Overlapping Windows on Semi-Local Region for Image Super-Resolution	There has been considerable progress in implicit neural representation to upscale an image to any arbitrary resolution. However, existing methods are based on defining a function to predict the Red, Green and Blue (RGB) value from just four specific loci. Relying on just four loci is insufficient as it leads to losing ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	406,491
2111.11517	Columnar Formats for Schemaless LSM-based Document Stores	In the last decade, document store database systems have gained more traction for storing and querying large volumes of semi-structured data. However, the flexibility of the document stores' data models has limited their ability to store data in a columnar-major layout - making them less performant for analytical workl...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	267,688
2104.09703	Bridging between soft and hard thresholding by scaling	In this article, we developed and analyzed a thresholding method in which soft thresholding estimators are independently expanded by empirical scaling values. The scaling values have a common hyper-parameter that is an order of expansion of an ideal scaling value that achieves hard thresholding. We simply call this est...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	231,322
1612.05143	Sampling-based Motion Planning for Active Multirotor System Identification	This paper reports on an algorithm for planning trajectories that allow a multirotor micro aerial vehicle (MAV) to quickly identify a set of unknown parameters. In many problems like self calibration or model parameter identification some states are only observable under a specific motion. These motions are often hard ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	65,641
1912.10169	A Comparison of Architectures and Pretraining Methods for Contextualized Multilingual Word Embeddings	The lack of annotated data in many languages is a well-known challenge within the field of multilingual natural language processing (NLP). Therefore, many recent studies focus on zero-shot transfer learning and joint training across languages to overcome data scarcity for low-resource languages. In this work we (i) per...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	158,253
2111.15452	On the Generalization of Agricultural Drought Classification from Climate Data	Climate change is expected to increase the likelihood of drought events, with severe implications for food security. Unlike other natural disasters, droughts have a slow onset and depend on various external factors, making drought detection in climate data difficult. In contrast to existing works that rely on simple re...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	268,933
1406.6844	FrameNet Resource Grammar Library for GF	In this paper we present an ongoing research investigating the possibility and potential of integrating frame semantics, particularly FrameNet, in the Grammatical Framework (GF) application grammar development. An important component of GF is its Resource Grammar Library (RGL) that encapsulates the low-level linguistic...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	34,160
2107.07737	EGC2: Enhanced Graph Classification with Easy Graph Compression	Graph classification is crucial in network analyses. Networks face potential security threats, such as adversarial attacks. Some defense methods may trade off the algorithm complexity for robustness, such as adversarial training, whereas others may trade off clean example performance, such as smoothingbased defense. Mo...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	246,512
2009.09335	Biomedical Event Extraction with Hierarchical Knowledge Graphs	Biomedical event extraction is critical in understanding biomolecular interactions described in scientific corpus. One of the main challenges is to identify nested structured events that are associated with non-indicative trigger words. We propose to incorporate domain knowledge from Unified Medical Language System (UM...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	196,544
2401.01801	A quatum inspired neural network for geometric modeling	By conceiving physical systems as 3D many-body point clouds, geometric graph neural networks (GNNs), such as SE(3)/E(3) equivalent GNNs, have showcased promising performance. In particular, their effective message-passing mechanics make them adept at modeling molecules and crystalline materials. However, current geomet...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	419,503
2202.10367	Probabilities of the Third Type: Statistical Relational Learning and Reasoning with Relative Frequencies	Dependencies on the relative frequency of a state in the domain are common when modelling probabilistic dependencies on relational data. For instance, the likelihood of a school closure during an epidemic might depend on the proportion of infected pupils exceeding a threshold. Often, rather than depending on discrete t...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	281,504
2004.00163	Weakly-Supervised Action Localization with Expectation-Maximization Multi-Instance Learning	Weakly-supervised action localization requires training a model to localize the action segments in the video given only video level action label. It can be solved under the Multiple Instance Learning (MIL) framework, where a bag (video) contains multiple instances (action segments). Since only the bag's label is known,...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	170,534
2402.08698	AMEND: A Mixture of Experts Framework for Long-tailed Trajectory Prediction	Accurate prediction of pedestrians' future motions is critical for intelligent driving systems. Developing models for this task requires rich datasets containing diverse sets of samples. However, the existing naturalistic trajectory prediction datasets are generally imbalanced in favor of simpler samples and lack chall...	false	false	false	false	false	false	true	true	false	false	false	true	false	false	false	false	false	false	429,199
2405.18663	Lifelong Learning and Selective Forgetting via Contrastive Strategy	Lifelong learning aims to train a model with good performance for new tasks while retaining the capacity of previous tasks. However, some practical scenarios require the system to forget undesirable knowledge due to privacy issues, which is called selective forgetting. The joint task of the two is dubbed Learning with ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	458,521
2310.15179	Reducing Uncertainty in Sea-level Rise Prediction: A Spatial-variability-aware Approach	Given multi-model ensemble climate projections, the goal is to accurately and reliably predict future sea-level rise while lowering the uncertainty. This problem is important because sea-level rise affects millions of people in coastal communities and beyond due to climate change's impacts on polar ice sheets and the o...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	402,205
2201.02450	Analytical calculation formulas for capacities of classical and classical-quantum channels	We derive an analytical calculation formula for the channel capacity of a classical channel without any iteration while its existing algorithms require iterations and the number of iteration depends on the required precision level. Hence, our formula is its first analytical formula without any iteration. We apply the o...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	274,548
2406.01917	GOMAA-Geo: GOal Modality Agnostic Active Geo-localization	We consider the task of active geo-localization (AGL) in which an agent uses a sequence of visual cues observed during aerial navigation to find a target specified through multiple possible modalities. This could emulate a UAV involved in a search-and-rescue operation navigating through an area, observing a stream of a...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	460,522
2306.14152	Low-Rank Prune-And-Factorize for Language Model Compression	The components underpinning PLMs -- large weight matrices -- were shown to bear considerable redundancy. Matrix factorization, a well-established technique from matrix theory, has been utilized to reduce the number of parameters in PLM. However, it fails to retain satisfactory performance under moderate to high compres...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	375,574
2212.14370	Can 5th Generation Local Training Methods Support Client Sampling? Yes!	The celebrated FedAvg algorithm of McMahan et al. (2017) is based on three components: client sampling (CS), data sampling (DS) and local training (LT). While the first two are reasonably well understood, the third component, whose role is to reduce the number of communication rounds needed to train the model, resisted...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	338,592
2410.21315	GraphLSS: Integrating Lexical, Structural, and Semantic Features for Long Document Extractive Summarization	Heterogeneous graph neural networks have recently gained attention for long document summarization, modeling the extraction as a node classification task. Although effective, these models often require external tools or additional machine learning models to define graph components, producing highly complex and less int...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	503,195
2204.11405	Adaptive cognitive fit: Artificial intelligence augmented management of information facets and representations	Explosive growth in big data technologies and artificial intelligence [AI] applications have led to increasing pervasiveness of information facets and a rapidly growing array of information representations. Information facets, such as equivocality and veracity, can dominate and significantly influence human perceptions...	true	false	false	true	true	false	false	false	false	true	false	false	false	true	false	false	false	false	293,132
2402.13724	Bring Your Own Character: A Holistic Solution for Automatic Facial Animation Generation of Customized Characters	Animating virtual characters has always been a fundamental research problem in virtual reality (VR). Facial animations play a crucial role as they effectively convey emotions and attitudes of virtual humans. However, creating such facial animations can be challenging, as current methods often involve utilization of exp...	true	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	431,385
2310.12563	Approximate information maximization for bandit games	Entropy maximization and free energy minimization are general physical principles for modeling the dynamics of various physical systems. Notable examples include modeling decision-making within the brain using the free-energy principle, optimizing the accuracy-complexity trade-off when accessing hidden variables with t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	401,072
2405.07072	Selecting focused digital cohorts from social media using the metric backbone of biomedical knowledge graphs	The abundance of social media data allows researchers to construct large digital cohorts to study the interplay between human behavior and medical treatment. Identifying the users most relevant to a specific health problem is, however, a challenge in that social media sites vary in the generality of their discourse. Wh...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	453,572
2402.17151	Clustering Document Parts: Detecting and Characterizing Influence Campaigns from Documents	We propose a novel clustering pipeline to detect and characterize influence campaigns from documents. This approach clusters parts of document, detects clusters that likely reflect an influence campaign, and then identifies documents linked to an influence campaign via their association with the high-influence clusters...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	432,859
2205.14894	Daisy Bloom Filters	A filter is a widely used data structure for storing an approximation of a given set $S$ of elements from some universe $U$ (a countable set).It represents a superset $S'\supseteq S$ that is ''close to $S$'' in the sense that for $x\not\in S$, the probability that $x\in S'$ is bounded by some $\varepsilon > 0$. The adv...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	true	299,531
2103.08095	Towards Robust Speech-to-Text Adversarial Attack	This paper introduces a novel adversarial algorithm for attacking the state-of-the-art speech-to-text systems, namely DeepSpeech, Kaldi, and Lingvo. Our approach is based on developing an extension for the conventional distortion condition of the adversarial optimization formulation using the Cram\`er integral probabil...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	224,793
2308.15645	AskIt: Unified Programming Interface for Programming with Large Language Models	Large Language Models (LLMs) exhibit a unique phenomenon known as emergent abilities, demonstrating adeptness across numerous tasks, from text summarization to code generation. While these abilities open up novel avenues in software design and crafting, their incorporation presents substantial challenges. Developers fa...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	388,742
2212.07429	Building Multilingual Corpora for a Complex Named Entity Recognition and Classification Hierarchy using Wikipedia and DBpedia	With the ever-growing popularity of the field of NLP, the demand for datasets in low resourced-languages follows suit. Following a previously established framework, in this paper, we present the UNER dataset, a multilingual and hierarchical parallel corpus annotated for named-entities. We describe in detail the develop...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	336,399
2406.16868	Neural Network-based Two-Dimensional Filtering for OTFS Symbol Detection	Orthogonal time frequency space (OTFS) is a promising modulation scheme for wireless communication in high-mobility scenarios. Recently, a reservoir computing (RC) based approach has been introduced for online subframe-based symbol detection in the OTFS system, where only the limited over-the-air (OTA) pilot symbols ar...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	467,310
2005.10247	Model-Based Robust Deep Learning: Generalizing to Natural, Out-of-Distribution Data	While deep learning has resulted in major breakthroughs in many application domains, the frameworks commonly used in deep learning remain fragile to artificially-crafted and imperceptible changes in the data. In response to this fragility, adversarial training has emerged as a principled approach for enhancing the robu...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	178,130
2012.00402	Use of Remote Sensing Data to Identify Air Pollution Signatures in India	Air quality has major impact on a country's socio-economic position and identifying major air pollution sources is at the heart of tackling the issue. Spatially and temporally distributed air quality data acquisition across a country as varied as India has been a challenge to such analysis. The launch of the Sentinel-5...	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	209,124
2203.10763	Performance-Robustness Tradeoffs in Adversarially Robust Linear-Quadratic Control	While $\mathcal{H}_\infty$ methods can introduce robustness against worst-case perturbations, their nominal performance under conventional stochastic disturbances is often drastically reduced. Though this fundamental tradeoff between nominal performance and robustness is known to exist, it is not well-characterized in ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	286,674
1503.00694	Consistent Probabilistic Social Choice	Two fundamental axioms in social choice theory are consistency with respect to a variable electorate and consistency with respect to components of similar alternatives. In the context of traditional non-probabilistic social choice, these axioms are incompatible with each other. We show that in the context of probabilis...	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	false	false	true	40,736
2101.11376	Learning Abstract Representations through Lossy Compression of Multi-Modal Signals	A key competence for open-ended learning is the formation of increasingly abstract representations useful for driving complex behavior. Abstract representations ignore specific details and facilitate generalization. Here we consider the learning of abstract representations in a multi-modal setting with two or more inpu...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	217,256
2303.09152	Learning a Room with the Occ-SDF Hybrid: Signed Distance Function Mingled with Occupancy Aids Scene Representation	Implicit neural rendering, which uses signed distance function (SDF) representation with geometric priors (such as depth or surface normal), has led to impressive progress in the surface reconstruction of large-scale scenes. However, applying this method to reconstruct a room-level scene from images may miss structures...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	351,931
2301.04653	Optirank: classification for RNA-Seq data with optimal ranking reference genes	Classification algorithms using RNA-Sequencing (RNA-Seq) data as input are used in a variety of biological applications. By nature, RNA-Seq data is subject to uncontrolled fluctuations both within and especially across datasets, which presents a major difficulty for a trained classifier to generalize to an external dat...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	340,134
2106.05249	What Would a Teacher Do? Predicting Future Talk Moves	Recent advances in natural language processing (NLP) have the ability to transform how classroom learning takes place. Combined with the increasing integration of technology in today's classrooms, NLP systems leveraging question answering and dialog processing techniques can serve as private tutors or participants in c...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	240,028
1403.6540	The quest for optimal sampling: Computationally efficient, structure-exploiting measurements for compressed sensing	An intriguing phenomenon in many instances of compressed sensing is that the reconstruction quality is governed not just by the overall sparsity of the signal, but also on its structure. This paper is about understanding this phenomenon, and demonstrating how it can be fruitfully exploited by the design of suitable sam...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	31,826
2208.04112	A review on longitudinal data analysis with random forest in precision medicine	Precision medicine provides customized treatments to patients based on their characteristics and is a promising approach to improving treatment efficiency. Large scale omics data are useful for patient characterization, but often their measurements change over time, leading to longitudinal data. Random forest is one of...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	311,997
2004.04814	Deep learning for synthetic microstructure generation in a materials-by-design framework for heterogeneous energetic materials	The sensitivity of heterogeneous energetic (HE) materials (propellants, explosives, and pyrotechnics) is critically dependent on their microstructure. Initiation of chemical reactions occurs at hot spots due to energy localization at sites of porosities and other defects. Emerging multi-scale predictive models of HE re...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	171,999
2209.08543	A Decoupled and Linear Framework for Global Outlier Rejection over Planar Pose Graph	We propose a robust framework for the planar pose graph optimization contaminated by loop closure outliers. Our framework rejects outliers by first decoupling the robust PGO problem wrapped by a Truncated Least Squares kernel into two subproblems. Then, the framework introduces a linear angle representation to rewrite ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	318,166
2310.18709	Audio-Visual Instance Segmentation	In this paper, we propose a new multi-modal task, termed audio-visual instance segmentation (AVIS), which aims to simultaneously identify, segment and track individual sounding object instances in audible videos. To facilitate this research, we introduce a high-quality benchmark named AVISeg, containing over 90K instan...	false	false	true	false	false	false	true	false	false	false	false	true	false	false	false	false	false	true	403,666
2211.12422	PiRL: Participant-Invariant Representation Learning for Healthcare	Due to individual heterogeneity, performance gaps are observed between generic (one-size-fits-all) models and person-specific models in data-driven health applications. However, in real-world applications, generic models are usually more favorable due to new-user-adaptation issues and system complexities, etc. To impro...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	332,103
2106.09082	Zeroth-Order Methods for Convex-Concave Minmax Problems: Applications to Decision-Dependent Risk Minimization	Min-max optimization is emerging as a key framework for analyzing problems of robustness to strategically and adversarially generated data. We propose a random reshuffling-based gradient free Optimistic Gradient Descent-Ascent algorithm for solving convex-concave min-max problems with finite sum structure. We prove t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	241,535
2410.11324	Diffusion-Based Offline RL for Improved Decision-Making in Augmented ARC Task	Effective long-term strategies enable AI systems to navigate complex environments by making sequential decisions over extended horizons. Similarly, reinforcement learning (RL) agents optimize decisions across sequences to maximize rewards, even without immediate feedback. To verify that Latent Diffusion-Constrained Q-l...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	498,506
1911.02265	Predictive modeling of brain tumor: A Deep learning approach	Image processing concepts can visualize the different anatomy structure of the human body. Recent advancements in the field of deep learning have made it possible to detect the growth of cancerous tissue just by a patient's brain Magnetic Resonance Imaging (MRI) scans. These methods require very high accuracy and meage...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	152,324
1908.00682	Attention Guided Low-light Image Enhancement with a Large Scale Low-light Simulation Dataset	Low-light image enhancement is challenging in that it needs to consider not only brightness recovery but also complex issues like color distortion and noise, which usually hide in the dark. Simply adjusting the brightness of a low-light image will inevitably amplify those artifacts. To address this difficult problem, t...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	140,562
1712.02449	Quantifying how much sensory information in a neural code is relevant for behavior	Determining how much of the sensory information carried by a neural code contributes to behavioral performance is key to understand sensory function and neural information flow. However, there are as yet no analytical tools to compute this information that lies at the intersection between sensory coding and behavioral ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	86,296
2306.16146	An optimal hierarchical control scheme for smart generation units: an application to combined steam and electricity generation	Optimal management of thermal and energy grids with fluctuating demand and prices requires to orchestrate the generation units (GU) among all their operating modes. A hierarchical approach is proposed to control coupled energy nonlinear systems. The high level hybrid optimization defines the unit commitment, with the o...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	376,295
1709.06772	Temporal Pattern Mining from Evolving Networks	Recently, evolving networks are becoming a suitable form to model many real-world complex systems, due to their peculiarities to represent the systems and their constituting entities, the interactions between the entities and the time-variability of their structure and properties. Designing computational models able to...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	81,171
1903.05784	Learning Parallax Attention for Stereo Image Super-Resolution	Stereo image pairs can be used to improve the performance of super-resolution (SR) since additional information is provided from a second viewpoint. However, it is challenging to incorporate this information for SR since disparities between stereo images vary significantly. In this paper, we propose a parallax-attentio...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	124,232
2402.07946	Re-Envisioning Command and Control	Future warfare will require Command and Control (C2) decision-making to occur in more complex, fast-paced, ill-structured, and demanding conditions. C2 will be further complicated by operational challenges such as Denied, Degraded, Intermittent, and Limited (DDIL) communications and the need to account for many data st...	true	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	428,910
2012.14337	WiFresh: Age-of-Information from Theory to Implementation	Emerging applications, such as smart factories and fleets of drones, increasingly rely on sharing time-sensitive information for monitoring and control. In such application domains, it is essential to keep information fresh, as outdated information loses its value and can lead to system failures and safety risks. The A...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	213,482
2302.12980	Frequency Disentangled Learning for Segmentation of Midbrain Structures from Quantitative Susceptibility Mapping Data	One often lacks sufficient annotated samples for training deep segmentation models. This is in particular the case for less common imaging modalities such as Quantitative Susceptibility Mapping (QSM). It has been shown that deep models tend to fit the target function from low to high frequencies. One may hypothesize th...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	347,761
2008.07672	Ensemble Node Embeddings using Tensor Decomposition: A Case-Study on DeepWalk	Node embeddings have been attracting increasing attention during the past years. In this context, we propose a new ensemble node embedding approach, called TenSemble2Vec, by first generating multiple embeddings using the existing techniques and taking them as multiview data input of the state-of-art tensor decompositio...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	192,180
2206.01739	Mutual- and Self- Prototype Alignment for Semi-supervised Medical Image Segmentation	Semi-supervised learning methods have been explored in medical image segmentation tasks due to the scarcity of pixel-level annotation in the real scenario. Proto-type alignment based consistency constraint is an intuitional and plausible solu-tion to explore the useful information in the unlabeled data. In this paper, ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	300,589
2104.06191	Lucas-Kanade Reloaded: End-to-End Super-Resolution from Raw Image Bursts	This presentation addresses the problem of reconstructing a high-resolution image from multiple lower-resolution snapshots captured from slightly different viewpoints in space and time. Key challenges for solving this problem include (i) aligning the input pictures with sub-pixel accuracy, (ii) handling raw (noisy) ima...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	229,991
2111.00653	SADGA: Structure-Aware Dual Graph Aggregation Network for Text-to-SQL	The Text-to-SQL task, aiming to translate the natural language of the questions into SQL queries, has drawn much attention recently. One of the most challenging problems of Text-to-SQL is how to generalize the trained model to the unseen database schemas, also known as the cross-domain Text-to-SQL task. The key lies in...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	264,291
1809.02648	Minimally Constrained Stable Switched Systems and Application to Co-simulation	We propose an algorithm to restrict the switching signals of a constrained switched system in order to guarantee its stability, while at the same time attempting to keep the largest possible set of allowed switching signals. Our work is motivated by applications to (co-)simulation, where numerical stability is a hard c...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	107,092
2411.15435	What Makes a Scene ? Scene Graph-based Evaluation and Feedback for Controllable Generation	While text-to-image generation has been extensively studied, generating images from scene graphs remains relatively underexplored, primarily due to challenges in accurately modeling spatial relationships and object interactions. To fill this gap, we introduce Scene-Bench, a comprehensive benchmark designed to evaluate ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	510,609
2110.03252	Layer-wise Pruning of Transformer Attention Heads for Efficient Language Modeling	While Transformer-based models have shown impressive language modeling performance, the large computation cost is often prohibitive for practical use. Attention head pruning, which removes unnecessary attention heads in the multihead attention, is a promising technique to solve this problem. However, it does not evenly...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	259,425
1707.00117	SAM: Semantic Attribute Modulation for Language Modeling and Style Variation	This paper presents a Semantic Attribute Modulation (SAM) for language modeling and style variation. The semantic attribute modulation includes various document attributes, such as titles, authors, and document categories. We consider two types of attributes, (title attributes and category attributes), and a flexible a...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	76,298
2106.10683	Solution for Large-scale Long-tailed Recognition with Noisy Labels	This is a technical report for CVPR 2021 AliProducts Challenge. AliProducts Challenge is a competition proposed for studying the large-scale and fine-grained commodity image recognition problem encountered by worldleading ecommerce companies. The large-scale product recognition simultaneously meets the challenge of noi...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	242,113
2406.19532	Dataless Quadratic Neural Networks for the Maximum Independent Set Problem	Combinatorial Optimization (CO) addresses many important problems, including the challenging Maximum Independent Set (MIS) problem. Alongside exact and heuristic solvers, differentiable approaches have emerged, often using continuous relaxations of ReLU-based or quadratic objectives. Noting that an MIS in a graph is a ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	468,451
2104.09938	Nonlinear Tracking and Rejection using Linear Parameter-Varying Control	The Linear Parameter-Varying (LPV) framework has been introduced with the intention to provide stability and performance guarantees for analysis and controller synthesis for Nonlinear (NL) systems via convex methods. By extending results of the Linear Time-Invariant framework, mainly based on quadratic stability and pe...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	231,408
2412.15129	Jet: A Modern Transformer-Based Normalizing Flow	In the past, normalizing generative flows have emerged as a promising class of generative models for natural images. This type of model has many modeling advantages: the ability to efficiently compute log-likelihood of the input data, fast generation and simple overall structure. Normalizing flows remained a topic of a...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	518,953
1207.0783	Hybrid Template Update System for Unimodal Biometric Systems	Semi-supervised template update systems allow to automatically take into account the intra-class variability of the biometric data over time. Such systems can be inefficient by including too many impostor's samples or skipping too many genuine's samples. In the first case, the biometric reference drifts from the real b...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	17,194
2007.01814	DynNet: Physics-based neural architecture design for linear and nonlinear structural response modeling and prediction	Data-driven models for predicting dynamic responses of linear and nonlinear systems are of great importance due to their wide application from probabilistic analysis to inverse problems such as system identification and damage diagnosis. In this study, a physics-based recurrent neural network model is designed that is ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	185,541
2009.07611	Perceiving Traffic from Aerial Images	Drones or UAVs, equipped with different sensors, have been deployed in many places especially for urban traffic monitoring or last-mile delivery. It provides the ability to control the different aspects of traffic given real-time obeservations, an important pillar for the future of transportation and smart cities. With...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	195,991
2205.13128	Cascading Residual Graph Convolutional Network for Multi-Behavior Recommendation	Multi-behavior recommendation exploits multiple types of user-item interactions to alleviate the data sparsity problem faced by the traditional models that often utilize only one type of interaction for recommendation. In real scenarios, users often take a sequence of actions to interact with an item, in order to get m...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	298,818
1909.07541	A*3D Dataset: Towards Autonomous Driving in Challenging Environments	With the increasing global popularity of self-driving cars, there is an immediate need for challenging real-world datasets for benchmarking and training various computer vision tasks such as 3D object detection. Existing datasets either represent simple scenarios or provide only day-time data. In this paper, we introdu...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	145,693
2311.10502	Fast Estimations of Hitting Time of Elitist Evolutionary Algorithms from Fitness Levels	The fitness level method is an easy-to-use tool for estimating the hitting time of elitist evolutionary algorithms. Recently, linear lower and upper bounds by fitness levels have been constructed. But these bounds require recursive computation, which makes them difficult to use in practice. We address this shortcoming ...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	408,548
2310.03037	Quantum image edge detection based on eight-direction Sobel operator for NEQR	Quantum Sobel edge detection (QSED) is a kind of algorithm for image edge detection using quantum mechanism, which can solve the real-time problem encountered by classical algorithms. However, the existing QSED algorithms only consider two- or four-direction Sobel operator, which leads to a certain loss of edge detail ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	397,111
2107.09760	An Exploration of Exploration: Measuring the ability of lexicase selection to find obscure pathways to optimality	Parent selection algorithms (selection schemes) steer populations through a problem's search space, often trading off between exploitation and exploration. Understanding how selection schemes affect exploitation and exploration within a search space is crucial to tackling increasingly challenging problems. Here, we int...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	247,113
2306.14219	Total Error Sheets for Datasets (TES-D) -- A Critical Guide to Documenting Online Platform Datasets	This paper proposes a template for documenting datasets that have been collected from online platforms for research purposes. The template should help to critically reflect on data quality and increase transparency in research fields that make use of online platform data. The paper describes our motivation, outlines th...	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	true	false	375,595
2305.17672	New Cycle-based Formulation, Cost Function, and Heuristics for DC OPF Based Controlled Islanding	This paper presents a new formulation for intentional controlled islanding (ICI) of power transmission grids based on mixed-integer linear programming (MILP) DC optimal power flow (OPF) model. We highlight several deficiencies of the most well-known formulation for this problem and propose new enhancements for their im...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	368,697
2106.05094	Semi-supervised lane detection with Deep Hough Transform	Current work on lane detection relies on large manually annotated datasets. We reduce the dependency on annotations by leveraging massive cheaply available unlabelled data. We propose a novel loss function exploiting geometric knowledge of lanes in Hough space, where a lane can be identified as a local maximum. By spli...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	239,967
2103.10284	SG-Net: Spatial Granularity Network for One-Stage Video Instance Segmentation	Video instance segmentation (VIS) is a new and critical task in computer vision. To date, top-performing VIS methods extend the two-stage Mask R-CNN by adding a tracking branch, leaving plenty of room for improvement. In contrast, we approach the VIS task from a new perspective and propose a one-stage spatial granulari...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	225,409
1704.06918	Neural Machine Translation via Binary Code Prediction	In this paper, we propose a new method for calculating the output layer in neural machine translation systems. The method is based on predicting a binary code for each word and can reduce computation time/memory requirements of the output layer to be logarithmic in vocabulary size in the best case. In addition, we also...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	72,255
2203.10229	Reinforcement Learned Distributed Multi-Robot Navigation with Reciprocal Velocity Obstacle Shaped Rewards	The challenges to solving the collision avoidance problem lie in adaptively choosing optimal robot velocities in complex scenarios full of interactive obstacles. In this paper, we propose a distributed approach for multi-robot navigation which combines the concept of reciprocal velocity obstacle (RVO) and the scheme of...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	286,453
0803.2973	Rule Generalisation in Intrusion Detection Systems using Snort	Intrusion Detection Systems (ids)provide an important layer of security for computer systems and networks, and are becoming more and more necessary as reliance on Internet services increases and systems with sensitive data are more commonly open to Internet access. An ids responsibility is to detect suspicious or unacc...	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	true	false	false	1,468
2011.09353	Generic Ontology Design Patterns: Roles and Change over Time	In this chapter we propose Generic Ontology Design Patterns, GODPs, as a methodology for representing and instantiating ontology design patterns in a way that is adaptable, and allows domain experts (and other users) to safely use them without cluttering their ontologies.	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	207,158
2003.01184	Variational inference formulation for a model-free simulation of a dynamical system with unknown parameters by a recurrent neural network	We propose a recurrent neural network for a "model-free" simulation of a dynamical system with unknown parameters without prior knowledge. The deep learning model aims to jointly learn the nonlinear time marching operator and the effects of the unknown parameters from a time series dataset. We assume that the time seri...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	166,562
2310.06572	Deep Learning reconstruction with uncertainty estimation for $\gamma$ photon interaction in fast scintillator detectors	This article presents a physics-informed deep learning method for the quantitative estimation of the spatial coordinates of gamma interactions within a monolithic scintillator, with a focus on Positron Emission Tomography (PET) imaging. A Density Neural Network approach is designed to estimate the 2-dimensional gamma p...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	398,635
2311.09783	Investigating Data Contamination in Modern Benchmarks for Large Language Models	Recent observations have underscored a disparity between the inflated benchmark scores and the actual performance of LLMs, raising concerns about potential contamination of evaluation benchmarks. This issue is especially critical for closed-source models and certain open-source models where training data transparency i...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	408,294
1910.03883	Second-order coding rates for key distillation in quantum key distribution	The security of quantum key distribution has traditionally been analyzed in either the asymptotic or non-asymptotic regimes. In this paper, we provide a bridge between these two regimes, by determining second-order coding rates for key distillation in quantum key distribution under collective attacks. Our main result i...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	148,620
2008.10224	Variable Compliance Control for Robotic Peg-in-Hole Assembly: A Deep Reinforcement Learning Approach	Industrial robot manipulators are playing a more significant role in modern manufacturing industries. Though peg-in-hole assembly is a common industrial task which has been extensively researched, safely solving complex high precision assembly in an unstructured environment remains an open problem. Reinforcement Learni...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	192,941
1708.00420	Impact of different time series aggregation methods on optimal energy system design	Modelling renewable energy systems is a computationally-demanding task due to the high fluctuation of supply and demand time series. To reduce the scale of these, this paper discusses different methods for their aggregation into typical periods. Each aggregation method is applied to a different type of energy system mo...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	78,209
1707.07301	Deep Optical Flow Estimation Via Multi-Scale Correspondence Structure Learning	As an important and challenging problem in computer vision, learning based optical flow estimation aims to discover the intrinsic correspondence structure between two adjacent video frames through statistical learning. Therefore, a key issue to solve in this area is how to effectively model the multi-scale corresponden...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	77,597

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