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Title: Transformer Network-based Reinforcement Learning Method for Power Distribution Network (PDN) Optimization of High Bandwidth Memory (HBM) Abstract: In this article, for the first time, we propose a transformer network-based reinforcement learning (RL) method for power distribution network (PDN) optimization of hi... |
Title: Synergizing Physics/Model-based and Data-driven Methods for Low-Dose CT Abstract: Since 2016, deep learning (DL) has advanced tomographic imaging with remarkable successes, especially in low-dose computed tomography (LDCT) imaging. Despite being driven by big data, the LDCT denoising and pure end-to-end reconstr... |
Title: Exact Community Recovery in Correlated Stochastic Block Models Abstract: We consider the problem of learning latent community structure from multiple correlated networks. We study edge-correlated stochastic block models with two balanced communities, focusing on the regime where the average degree is logarithmic... |
Title: Towards Spatio-Temporal Aware Traffic Time Series Forecasting--Full Version Abstract: Traffic time series forecasting is challenging due to complex spatio-temporal dynamics time series from different locations often have distinct patterns; and for the same time series, patterns may vary across time, where, for e... |
Title: A Passive Similarity based CNN Filter Pruning for Efficient Acoustic Scene Classification Abstract: We present a method to develop low-complexity convolutional neural networks (CNNs) for acoustic scene classification (ASC). The large size and high computational complexity of typical CNNs is a bottleneck for thei... |
Title: HardVis: Visual Analytics to Handle Instance Hardness Using Undersampling and Oversampling Techniques Abstract: Despite the tremendous advances in machine learning (ML), training with imbalanced data still poses challenges in many real-world applications. Among a series of diverse techniques to solve this proble... |
Title: Evaluating Prompts Across Multiple Choice Tasks In a Zero-Shot Setting Abstract: Large language models have shown that impressive zero-shot performance can be achieved through natural language prompts (Radford et al., 2019; Brown et al., 2020; Sanh et al., 2021). Creating an effective prompt, however, requires s... |
Title: Causal de Finetti: On the Identification of Invariant Causal Structure in Exchangeable Data Abstract: Learning invariant causal structure often relies on conditional independence testing and assumption of independent and identically distributed data. Recent work has explored inferring invariant causal structure ... |
Title: A Sparsity-promoting Dictionary Model for Variational Autoencoders Abstract: Structuring the latent space in probabilistic deep generative models, e.g., variational autoencoders (VAEs), is important to yield more expressive models and interpretable representations, and to avoid overfitting. One way to achieve th... |
Title: Implementation of an Automated Learning System for Non-experts Abstract: Automated machine learning systems for non-experts could be critical for industries to adopt artificial intelligence to their own applications. This paper detailed the engineering system implementation of an automated machine learning syste... |
Title: Revisiting Neighborhood-based Link Prediction for Collaborative Filtering Abstract: Collaborative filtering (CF) is one of the most successful and fundamental techniques in recommendation systems. In recent years, Graph Neural Network (GNN)-based CF models, such as NGCF [31], LightGCN [10] and GTN [9] have achie... |
Title: Convergence and Complexity of Stochastic Subgradient Methods with Dependent Data for Nonconvex Optimization Abstract: We show that under a general dependent data sampling scheme, the classical stochastic projected and proximal subgradient methods for weakly convex functions have worst-case rate of convergence $\... |
Title: Shift-Robust Node Classification via Graph Adversarial Clustering Abstract: Graph Neural Networks (GNNs) are de facto node classification models in graph structured data. However, during testing-time, these algorithms assume no data shift, i.e., $\Pr_\text{train}(X,Y) = \Pr_\text{test}(X,Y)$. Domain adaption met... |
Title: Improving The Diagnosis of Thyroid Cancer by Machine Learning and Clinical Data Abstract: Thyroid cancer is a common endocrine carcinoma that occurs in the thyroid gland. Much effort has been invested in improving its diagnosis, and thyroidectomy remains the primary treatment method. A successful operation witho... |
Title: LinkBERT: Pretraining Language Models with Document Links Abstract: Language model (LM) pretraining can learn various knowledge from text corpora, helping downstream tasks. However, existing methods such as BERT model a single document, and do not capture dependencies or knowledge that span across documents. In ... |
Title: Learning to Collide: Recommendation System Model Compression with Learned Hash Functions Abstract: A key characteristic of deep recommendation models is the immense memory requirements of their embedding tables. These embedding tables can often reach hundreds of gigabytes which increases hardware requirements an... |
Title: NNLander-VeriF: A Neural Network Formal Verification Framework for Vision-Based Autonomous Aircraft Landing Abstract: In this paper, we consider the problem of formally verifying a Neural Network (NN) based autonomous landing system. In such a system, a NN controller processes images from a camera to guide the a... |
Title: Topological Experience Replay Abstract: State-of-the-art deep Q-learning methods update Q-values using state transition tuples sampled from the experience replay buffer. This strategy often uniformly and randomly samples or prioritizes data sampling based on measures such as the temporal difference (TD) error. S... |
Title: Near-optimality for infinite-horizon restless bandits with many arms Abstract: Restless bandits are an important class of problems with applications in recommender systems, active learning, revenue management and other areas. We consider infinite-horizon discounted restless bandits with many arms where a fixed p... |
Title: Investigating Data Variance in Evaluations of Automatic Machine Translation Metrics Abstract: Current practices in metric evaluation focus on one single dataset, e.g., Newstest dataset in each year's WMT Metrics Shared Task. However, in this paper, we qualitatively and quantitatively show that the performances o... |
Title: NICGSlowDown: Evaluating the Efficiency Robustness of Neural Image Caption Generation Models Abstract: Neural image caption generation (NICG) models have received massive attention from the research community due to their excellent performance in visual understanding. Existing work focuses on improving NICG mode... |
Title: Visualizing the Relationship Between Encoded Linguistic Information and Task Performance Abstract: Probing is popular to analyze whether linguistic information can be captured by a well-trained deep neural model, but it is hard to answer how the change of the encoded linguistic information will affect task perfo... |
Title: An Overview & Analysis of Sequence-to-Sequence Emotional Voice Conversion Abstract: Emotional voice conversion (EVC) focuses on converting a speech utterance from a source to a target emotion; it can thus be a key enabling technology for human-computer interaction applications and beyond. However, EVC remains an... |
Title: Radial Autoencoders for Enhanced Anomaly Detection Abstract: In classification problems, supervised machine-learning methods outperform traditional algorithms, thanks to the ability of neural networks to learn complex patterns. However, in two-class classification tasks like anomaly or fraud detection, unsupervi... |
Title: Pretraining Graph Neural Networks for few-shot Analog Circuit Modeling and Design Abstract: Being able to predict the performance of circuits without running expensive simulations is a desired capability that can catalyze automated design. In this paper, we present a supervised pretraining approach to learn circ... |
Title: Multi-Agent Asynchronous Cooperation with Hierarchical Reinforcement Learning Abstract: Hierarchical multi-agent reinforcement learning (MARL) has shown a significant learning efficiency by searching policy over higher-level, temporally extended actions (options). However, standard policy gradient-based MARL met... |
Title: Self-Contrastive Learning based Semi-Supervised Radio Modulation Classification Abstract: This paper presents a semi-supervised learning framework that is new in being designed for automatic modulation classification (AMC). By carefully utilizing unlabeled signal data with a self-supervised contrastive-learning ... |
Title: Graph Neural Networks in IoT: A Survey Abstract: The Internet of Things (IoT) boom has revolutionized almost every corner of people's daily lives: healthcare, home, transportation, manufacturing, supply chain, and so on. With the recent development of sensor and communication technologies, IoT devices including ... |
Title: A Simple Yet Effective Pretraining Strategy for Graph Few-shot Learning Abstract: Recently, increasing attention has been devoted to the graph few-shot learning problem, where the target novel classes only contain a few labeled nodes. Among many existing endeavors, episodic meta-learning has become the most prev... |
Title: Improving Mispronunciation Detection with Wav2vec2-based Momentum Pseudo-Labeling for Accentedness and Intelligibility Assessment Abstract: Current leading mispronunciation detection and diagnosis (MDD) systems achieve promising performance via end-to-end phoneme recognition. One challenge of such end-to-end sol... |
Title: Robust, Automated, and Accurate Black-box Variational Inference Abstract: Black-box variational inference (BBVI) now sees widespread use in machine learning and statistics as a fast yet flexible alternative to Markov chain Monte Carlo methods for approximate Bayesian inference. However, stochastic optimization m... |
Title: 4-bit Conformer with Native Quantization Aware Training for Speech Recognition Abstract: Reducing the latency and model size has always been a significant research problem for live Automatic Speech Recognition (ASR) application scenarios. Along this direction, model quantization has become an increasingly popula... |
Title: Investigating the Properties of Neural Network Representations in Reinforcement Learning Abstract: In this paper we investigate the properties of representations learned by deep reinforcement learning systems. Much of the earlier work in representation learning for reinforcement learning focused on designing fix... |
Title: Entity-driven Fact-aware Abstractive Summarization of Biomedical Literature Abstract: As part of the large number of scientific articles being published every year, the publication rate of biomedical literature has been increasing. Consequently, there has been considerable effort to harness and summarize the mas... |
Title: Higher-Order Generalization Bounds: Learning Deep Probabilistic Programs via PAC-Bayes Objectives Abstract: Deep Probabilistic Programming (DPP) allows powerful models based on recursive computation to be learned using efficient deep-learning optimization techniques. Additionally, DPP offers a unified perspectiv... |
Title: Device-Directed Speech Detection: Regularization via Distillation for Weakly-Supervised Models Abstract: We address the problem of detecting speech directed to a device that does not contain a specific wake-word. Specifically, we focus on audio coming from a touch-based invocation. Mitigating virtual assistants ... |
Title: DELTA: Dynamically Optimizing GPU Memory beyond Tensor Recomputation Abstract: The further development of deep neural networks is hampered by the limited GPU memory resource. Therefore, the optimization of GPU memory resources is highly demanded. Swapping and recomputation are commonly applied to make better use... |
Title: VI-IKD: High-Speed Accurate Off-Road Navigation using Learned Visual-Inertial Inverse Kinodynamics Abstract: One of the key challenges in high speed off road navigation on ground vehicles is that the kinodynamics of the vehicle terrain interaction can differ dramatically depending on the terrain. Previous approa... |
Title: Optimal Learning Abstract: This paper studies the problem of learning an unknown function $f$ from given data about $f$. The learning problem is to give an approximation $\hat f$ to $f$ that predicts the values of $f$ away from the data. There are numerous settings for this learning problem depending on (i) what... |
Title: Meta-Sampler: Almost-Universal yet Task-Oriented Sampling for Point Clouds Abstract: Sampling is a key operation in point-cloud task and acts to increase computational efficiency and tractability by discarding redundant points. Universal sampling algorithms (e.g., Farthest Point Sampling) work without modificati... |
Title: Theory of Acceleration of Decision Making by Correlated Time Sequences Abstract: Photonic accelerators have been intensively studied to provide enhanced information processing capability to benefit from the unique attributes of physical processes. Recently, it has been reported that chaotically oscillating ultra... |
Title: Prognosis of Rotor Parts Fly-off Based on Cascade Classification and Online Prediction Ability Index Abstract: Large rotating machines, e.g., compressors, steam turbines, gas turbines, are critical equipment in many process industries such as energy, chemical, and power generation. Due to high rotating speed and... |
Title: Towards Collaborative Intelligence: Routability Estimation based on Decentralized Private Data Abstract: Applying machine learning (ML) in design flow is a popular trend in EDA with various applications from design quality predictions to optimizations. Despite its promise, which has been demonstrated in both aca... |
Title: Longitudinal Fairness with Censorship Abstract: Recent works in artificial intelligence fairness attempt to mitigate discrimination by proposing constrained optimization programs that achieve parity for some fairness statistic. Most assume availability of the class label, which is impractical in many real-world ... |
Title: How Deep is Your Art: An Experimental Study on the Limits of Artistic Understanding in a Single-Task, Single-Modality Neural Network Abstract: Mathematical modeling and aesthetic rule extraction of works of art are complex activities. This is because art is a multidimensional, subjective discipline. Perception a... |
Title: Monitored Distillation for Positive Congruent Depth Completion Abstract: We propose a method to infer a dense depth map from a single image, its calibration, and the associated sparse point cloud. In order to leverage existing models that produce putative depth maps (teacher models), we propose an adaptive knowl... |
Title: Enhancing Zero-Shot Many to Many Voice Conversion with Self-Attention VAE Abstract: Variational auto-encoder(VAE) is an effective neural network architecture to disentangle a speech utterance into speaker identity and linguistic content latent embeddings, then generate an utterance for a target speaker from that... |
Title: Disentangling the Impacts of Language and Channel Variability on Speech Separation Networks Abstract: Because the performance of speech separation is excellent for speech in which two speakers completely overlap, research attention has been shifted to dealing with more realistic scenarios. However, domain mismat... |
Title: Coarse-to-Fine Recursive Speech Separation for Unknown Number of Speakers Abstract: The vast majority of speech separation methods assume that the number of speakers is known in advance, hence they are specific to the number of speakers. By contrast, a more realistic and challenging task is to separate a mixture... |
Title: Decision-Focused Learning without Decision-Making: Learning Locally Optimized Decision Losses Abstract: Decision-Focused Learning (DFL) is a paradigm for tailoring a predictive model to a downstream optimization task that uses its predictions in order to perform better on that specific task. The main technical c... |
Title: An Improved Greedy Algorithm for Subset Selection in Linear Estimation Abstract: In this paper, we consider a subset selection problem in a spatial field where we seek to find a set of k locations whose observations provide the best estimate of the field value at a finite set of prediction locations. The measure... |
Title: Explainable Artificial Intelligence in Process Mining: Assessing the Explainability-Performance Trade-Off in Outcome-Oriented Predictive Process Monitoring Abstract: Recently, a shift has been made in the field of Outcome-Oriented Predictive Process Monitoring (OOPPM) to use models from the eXplainable Artificia... |
Title: STRPM: A Spatiotemporal Residual Predictive Model for High-Resolution Video Prediction Abstract: Although many video prediction methods have obtained good performance in low-resolution (64$\sim$128) videos, predictive models for high-resolution (512$\sim$4K) videos have not been fully explored yet, which are mor... |
Title: Polarized deep diffractive neural network for classification, generation, multiplexing and de-multiplexing of orbital angular momentum modes Abstract: The multiplexing and de-multiplexing of orbital angular momentum (OAM) beams are critical issues in optical communication. Optical diffractive neural networks hav... |
Title: Neighbor Enhanced Graph Convolutional Networks for Node Classification and Recommendation Abstract: The recently proposed Graph Convolutional Networks (GCNs) have achieved significantly superior performance on various graph-related tasks, such as node classification and recommendation. However, currently researc... |
Title: Adaptive Private-K-Selection with Adaptive K and Application to Multi-label PATE Abstract: We provide an end-to-end Renyi DP based-framework for differentially private top-$k$ selection. Unlike previous approaches, which require a data-independent choice on $k$, we propose to privately release a data-dependent c... |
Title: Continual Normalization: Rethinking Batch Normalization for Online Continual Learning Abstract: Existing continual learning methods use Batch Normalization (BN) to facilitate training and improve generalization across tasks. However, the non-i.i.d and non-stationary nature of continual learning data, especially ... |
Title: Improving Distortion Robustness of Self-supervised Speech Processing Tasks with Domain Adaptation Abstract: Speech distortions are a long-standing problem that degrades the performance of supervisely trained speech processing models. It is high time that we enhance the robustness of speech processing models to o... |
Title: Weakly-supervised Temporal Path Representation Learning with Contrastive Curriculum Learning -- Extended Version Abstract: In step with the digitalization of transportation, we are witnessing a growing range of path-based smart-city applications, e.g., travel-time estimation and travel path ranking. A temporal p... |
Title: A Fast Transformer-based General-Purpose Lossless Compressor Abstract: Deep-learning-based compressor has received interests recently due to much improved compression ratio. However, modern approaches suffer from long execution time. To ease this problem, this paper targets on cutting down the execution time of ... |
Title: Example-based Explanations with Adversarial Attacks for Respiratory Sound Analysis Abstract: Respiratory sound classification is an important tool for remote screening of respiratory-related diseases such as pneumonia, asthma, and COVID-19. To facilitate the interpretability of classification results, especially... |
Title: Tampered VAE for Improved Satellite Image Time Series Classification Abstract: The unprecedented availability of spatial and temporal high-resolution satellite image time series (SITS) for crop type mapping is believed to necessitate deep learning architectures to accommodate challenges arising from both dimensi... |
Title: RICON: A ML framework for real-time and proactive intervention to prevent customer churn Abstract: We consider the problem of churn prediction in real-time. Because of the batch mode of inference generation, the traditional methods can only support retention campaigns with offline interventions, e.g., test messa... |
Title: Recommendation of Compatible Outfits Conditioned on Style Abstract: Recommendation in the fashion domain has seen a recent surge in research in various areas, for example, shop-the-look, context-aware outfit creation, personalizing outfit creation, etc. The majority of state of the art approaches in the domain o... |
Title: AdaGrid: Adaptive Grid Search for Link Prediction Training Objective Abstract: One of the most important factors that contribute to the success of a machine learning model is a good training objective. Training objective crucially influences the model's performance and generalization capabilities. This paper spe... |
Title: Marginalized Operators for Off-policy Reinforcement Learning Abstract: In this work, we propose marginalized operators, a new class of off-policy evaluation operators for reinforcement learning. Marginalized operators strictly generalize generic multi-step operators, such as Retrace, as special cases. Marginaliz... |
Title: Dynamic Model Tree for Interpretable Data Stream Learning Abstract: Data streams are ubiquitous in modern business and society. In practice, data streams may evolve over time and cannot be stored indefinitely. Effective and transparent machine learning on data streams is thus often challenging. Hoeffding Trees h... |
Title: Automatic Identification of Chemical Moieties Abstract: In recent years, the prediction of quantum mechanical observables with machine learning methods has become increasingly popular. Message-passing neural networks (MPNNs) solve this task by constructing atomic representations, from which the properties of int... |
Title: Adaptive Divergence-based Non-negative Latent Factor Analysis Abstract: High-Dimensional and Incomplete (HDI) data are frequently found in various industrial applications with complex interactions among numerous nodes, which are commonly non-negative for representing the inherent non-negativity of node interacti... |
Title: Improved Convergence Rate of Stochastic Gradient Langevin Dynamics with Variance Reduction and its Application to Optimization Abstract: The stochastic gradient Langevin Dynamics is one of the most fundamental algorithms to solve sampling problems and non-convex optimization appearing in several machine learning... |
Title: APG: Adaptive Parameter Generation Network for Click-Through Rate Prediction Abstract: In many web applications, deep learning-based CTR prediction models (deep CTR models for short) are widely adopted. Traditional deep CTR models learn patterns in a static manner, i.e., the network parameters are the same acros... |
Title: Phase-Aware Deep Speech Enhancement: It's All About The Frame Length Abstract: While phase-aware speech processing has been receiving increasing attention in recent years, most narrowband STFT approaches with frame lengths of about 32ms show a rather modest impact of phase on overall performance. At the same tim... |
Title: Hypergraphon Mean Field Games Abstract: We propose an approach to modelling large-scale multi-agent dynamical systems allowing interactions among more than just pairs of agents using the theory of mean-field games and the notion of hypergraphons, which are obtained as limits of large hypergraphs. To the best of ... |
Title: Biclustering Algorithms Based on Metaheuristics: A Review Abstract: Biclustering is an unsupervised machine learning technique that simultaneously clusters rows and columns in a data matrix. Biclustering has emerged as an important approach and plays an essential role in various applications such as bioinformati... |
Title: Co-Membership-based Generic Anomalous Communities Detection Abstract: Nowadays, detecting anomalous communities in networks is an essential task in research, as it helps discover insights into community-structured networks. Most of the existing methods leverage either information regarding attributes of vertices... |
Title: Physics Community Needs, Tools, and Resources for Machine Learning Abstract: Machine learning (ML) is becoming an increasingly important component of cutting-edge physics research, but its computational requirements present significant challenges. In this white paper, we discuss the needs of the physics communit... |
Title: Image-to-Lidar Self-Supervised Distillation for Autonomous Driving Data Abstract: Segmenting or detecting objects in sparse Lidar point clouds are two important tasks in autonomous driving to allow a vehicle to act safely in its 3D environment. The best performing methods in 3D semantic segmentation or object de... |
Title: How Does SimSiam Avoid Collapse Without Negative Samples? A Unified Understanding with Self-supervised Contrastive Learning Abstract: To avoid collapse in self-supervised learning (SSL), a contrastive loss is widely used but often requires a large number of negative samples. Without negative samples yet achievin... |
Title: Does Audio Deepfake Detection Generalize? Abstract: Current text-to-speech algorithms produce realistic fakes of human voices, making deepfake detection a much-needed area of research. While researchers have presented various techniques for detecting audio spoofs, it is often unclear exactly why these architectu... |
Title: Reinforcement Learning Guided by Provable Normative Compliance Abstract: Reinforcement learning (RL) has shown promise as a tool for engineering safe, ethical, or legal behaviour in autonomous agents. Its use typically relies on assigning punishments to state-action pairs that constitute unsafe or unethical choi... |
Title: The Weak Supervision Landscape Abstract: Many ways of annotating a dataset for machine learning classification tasks that go beyond the usual class labels exist in practice. These are of interest as they can simplify or facilitate the collection of annotations, while not greatly affecting the resulting machine l... |
Title: An Offset-Free Nonlinear MPC scheme for systems learned by Neural NARX models Abstract: This paper deals with the design of nonlinear MPC controllers that provide offset-free setpoint tracking for models described by Neural Nonlinear AutoRegressive eXogenous (NNARX) networks. The NNARX model is identified from i... |
Title: Context-aware Automatic Music Transcription Abstract: This paper presents an Automatic Music Transcription system that incorporates context-related information. Motivated by the state-of-art psychological research, we propose a methodology boosting the accuracy of AMT systems by modeling the adaptations that per... |
Title: Forecasting from LiDAR via Future Object Detection Abstract: Object detection and forecasting are fundamental components of embodied perception. These two problems, however, are largely studied in isolation by the community. In this paper, we propose an end-to-end approach for detection and motion forecasting ba... |
Title: Zero-shot meta-learning for small-scale data from human subjects Abstract: While developments in machine learning led to impressive performance gains on big data, many human subjects data are, in actuality, small and sparsely labeled. Existing methods applied to such data often do not easily generalize to out-of... |
Title: When to Go, and When to Explore: The Benefit of Post-Exploration in Intrinsic Motivation Abstract: Go-Explore achieved breakthrough performance on challenging reinforcement learning (RL) tasks with sparse rewards. The key insight of Go-Explore was that successful exploration requires an agent to first return to ... |
Title: A Multi-Stage Duplex Fusion ConvNet for Aerial Scene Classification Abstract: Existing deep learning based methods effectively prompt the performance of aerial scene classification. However, due to the large amount of parameters and computational cost, it is rather difficult to apply these methods to multiple re... |
Title: Smooth Robust Tensor Completion for Background/Foreground Separation with Missing Pixels: Novel Algorithm with Convergence Guarantee Abstract: The objective of this study is to address the problem of background/foreground separation with missing pixels by combining the video acquisition, video recovery, backgrou... |
Title: FlexFringe: Modeling Software Behavior by Learning Probabilistic Automata Abstract: We present the efficient implementations of probabilistic deterministic finite automaton learning methods available in FlexFringe. These implement well-known strategies for state-merging including several modifications to improve... |
Title: Instantaneous Frequency Estimation In Multi-Component Signals Using Stochastic EM Algorithm Abstract: This paper addresses the problem of estimating the modes of an observed non-stationary mixture signal in the presence of an arbitrary distributed noise. A novel Bayesian model is introduced to estimate the model... |
Title: TraHGR: Transformer for Hand Gesture Recognition via ElectroMyography Abstract: Deep learning-based Hand Gesture Recognition (HGR) via surface Electromyogram (sEMG) signals has recently shown significant potential for development of advanced myoelectric-controlled prosthesis. Existing deep learning approaches, t... |
Title: Stack operation of tensor networks Abstract: The tensor network, as a facterization of tensors, aims at performing the operations that are common for normal tensors, such as addition, contraction and stacking. However, due to its non-unique network structure, only the tensor network contraction is so far well de... |
Title: Robust and Energy-efficient PPG-based Heart-Rate Monitoring Abstract: A wrist-worn PPG sensor coupled with a lightweight algorithm can run on a MCU to enable non-invasive and comfortable monitoring, but ensuring robust PPG-based heart-rate monitoring in the presence of motion artifacts is still an open challenge... |
Title: Optimization for Classical Machine Learning Problems on the GPU Abstract: Constrained optimization problems arise frequently in classical machine learning. There exist frameworks addressing constrained optimization, for instance, CVXPY and GENO. However, in contrast to deep learning frameworks, GPU support is li... |
Title: Learning multiobjective rough terrain traversability Abstract: We present a method that uses high-resolution topography data of rough terrain, and ground vehicle simulation, to predict traversability. Traversability is expressed as three independent measures: the ability to traverse the terrain at a target speed... |
Title: Generative Adversarial Networks for the fast simulation of the Time Projection Chamber responses at the MPD detector Abstract: The detailed detector simulation models are vital for the successful operation of modern high-energy physics experiments. In most cases, such detailed models require a significant amount... |
Title: IGRF-RFE: A Hybrid Feature Selection Method for MLP-based Network Intrusion Detection on UNSW-NB15 Dataset Abstract: The effectiveness of machine learning models is significantly affected by the size of the dataset and the quality of features as redundant and irrelevant features can radically degrade the perform... |
Title: Slow-varying Dynamics Assisted Temporal Capsule Network for Machinery Remaining Useful Life Estimation Abstract: Capsule network (CapsNet) acts as a promising alternative to the typical convolutional neural network, which is the dominant network to develop the remaining useful life (RUL) estimation models for me... |
Title: Online Motion Style Transfer for Interactive Character Control Abstract: Motion style transfer is highly desired for motion generation systems for gaming. Compared to its offline counterpart, the research on online motion style transfer under interactive control is limited. In this work, we propose an end-to-end... |
Title: PerfectDou: Dominating DouDizhu with Perfect Information Distillation Abstract: As a challenging multi-player card game, DouDizhu has recently drawn much attention for analyzing competition and collaboration in imperfect-information games. In this paper, we propose PerfectDou, a state-of-the-art DouDizhu AI syst... |
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