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Title: RareGAN: Generating Samples for Rare Classes Abstract: We study the problem of learning generative adversarial networks (GANs) for a rare class of an unlabeled dataset subject to a labeling budget. This problem is motivated from practical applications in domains including security (e.g., synthesizing packets for... |
Title: Repairing Brain-Computer Interfaces with Fault-Based Data Acquisition Abstract: Brain-computer interfaces (BCIs) decode recorded neural signals from the brain and/or stimulate the brain with encoded neural signals. BCIs span both hardware and software and have a wide range of applications in restorative medicine... |
Title: Learning latent causal relationships in multiple time series Abstract: Identifying the causal structure of systems with multiple dynamic elements is critical to several scientific disciplines. The conventional approach is to conduct statistical tests of causality, for example with Granger Causality, between obse... |
Title: Online Continual Learning for Embedded Devices Abstract: Real-time on-device continual learning is needed for new applications such as home robots, user personalization on smartphones, and augmented/virtual reality headsets. However, this setting poses unique challenges: embedded devices have limited memory and ... |
Title: CNN Attention Guidance for Improved Orthopedics Radiographic Fracture Classification Abstract: Convolutional neural networks (CNNs) have gained significant popularity in orthopedic imaging in recent years due to their ability to solve fracture classification problems. A common criticism of CNNs is their opaque l... |
Title: Monocular Vision-based Prediction of Cut-in Maneuvers with LSTM Networks Abstract: Advanced driver assistance and automated driving systems should be capable of predicting and avoiding dangerous situations. This study proposes a method to predict potentially dangerous cut-in maneuvers happening in the ego lane. ... |
Title: Forecast Evaluation for Data Scientists: Common Pitfalls and Best Practices Abstract: Machine Learning (ML) and Deep Learning (DL) methods are increasingly replacing traditional methods in many domains involved with important decision making activities. DL techniques tailor-made for specific tasks such as image ... |
Title: Prediction Algorithm for Heat Demand of Science and Technology Topics Based on Time Convolution Network Abstract: Thanks to the rapid development of deep learning, big data analysis technology is not only widely used in the field of natural language processing, but also more mature in the field of numerical pred... |
Title: An Intermediate-level Attack Framework on The Basis of Linear Regression Abstract: This paper substantially extends our work published at ECCV, in which an intermediate-level attack was proposed to improve the transferability of some baseline adversarial examples. We advocate to establish a direct linear mapping... |
Title: The activity-weight duality in feed forward neural networks: The geometric determinants of generalization Abstract: One of the fundamental problems in machine learning is generalization. In neural network models with a large number of weights (parameters), many solutions can be found to fit the training data equ... |
Title: STCGAT: Spatial-temporal causal networks for complex urban road traffic flow prediction Abstract: Traffic forecasting is an essential component of intelligent transportation systems. However, traffic data are highly nonlinear and have complex spatial correlations between road nodes. Therefore, it is incredibly c... |
Title: A Slot Is Not Built in One Utterance: Spoken Language Dialogs with Sub-Slots Abstract: A slot value might be provided segment by segment over multiple-turn interactions in a dialog, especially for some important information such as phone numbers and names. It is a common phenomenon in daily life, but little atte... |
Title: Decoupled Mixup for Data-efficient Learning Abstract: Mixup is an efficient data augmentation approach that improves the generalization of neural networks by smoothing the decision boundary with mixed data. Recently, dynamic mixup methods improve previous static policies (e.g., linear interpolation) by maximizin... |
Title: ASE: Anomaly Scoring Based Ensemble Learning for Imbalanced Datasets Abstract: Nowadays, many industries have applied classification algorithms to help them solve problems in their business, like finance, medicine, manufacturing industry and so on. However, in real-life scenarios, positive examples only make up ... |
Title: Slice Imputation: Intermediate Slice Interpolation for Anisotropic 3D Medical Image Segmentation Abstract: We introduce a novel frame-interpolation-based method for slice imputation to improve segmentation accuracy for anisotropic 3D medical images, in which the number of slices and their corresponding segmentat... |
Title: Delving into the Estimation Shift of Batch Normalization in a Network Abstract: Batch normalization (BN) is a milestone technique in deep learning. It normalizes the activation using mini-batch statistics during training but the estimated population statistics during inference. This paper focuses on investigatin... |
Title: Classifications of Skull Fractures using CT Scan Images via CNN with Lazy Learning Approach Abstract: Classification of skull fracture is a challenging task for both radiologists and researchers. Skull fractures result in broken pieces of bone, which can cut into the brain and cause bleeding and other injury typ... |
Title: Domain Generalization by Mutual-Information Regularization with Pre-trained Models Abstract: Domain generalization (DG) aims to learn a generalized model to an unseen target domain using only limited source domains. Previous attempts to DG fail to learn domain-invariant representations only from the source domai... |
Title: Graph Neural Networks for Wireless Communications: From Theory to Practice Abstract: Deep learning-based approaches have been developed to solve challenging problems in wireless communications, leading to promising results. Early attempts adopted neural network architectures inherited from applications such as c... |
Title: AnoViT: Unsupervised Anomaly Detection and Localization with Vision Transformer-based Encoder-Decoder Abstract: Image anomaly detection problems aim to determine whether an image is abnormal, and to detect anomalous areas. These methods are actively used in various fields such as manufacturing, medical care, and... |
Title: Long Short-Term Memory for Spatial Encoding in Multi-Agent Path Planning Abstract: Reinforcement learning-based path planning for multi-agent systems of varying size constitutes a research topic with increasing significance as progress in domains such as urban air mobility and autonomous aerial vehicles continue... |
Title: Perceptual Features as Markers of Parkinson's Disease: The Issue of Clinical Interpretability Abstract: Up to 90% of patients with Parkinson's disease (PD) suffer from hypokinetic dysathria (HD) which is also manifested in the field of phonation. Clinical signs of HD like monoloudness, monopitch or hoarse voice ... |
Title: Hyperbolic Vision Transformers: Combining Improvements in Metric Learning Abstract: Metric learning aims to learn a highly discriminative model encouraging the embeddings of similar classes to be close in the chosen metrics and pushed apart for dissimilar ones. The common recipe is to use an encoder to extract e... |
Title: Multi-class versus One-class classifier in spontaneous speech analysis oriented to Alzheimer Disease diagnosis Abstract: Most of medical developments require the ability to identify samples that are anomalous with respect to a target group or control group, in the sense they could belong to a new, previously uns... |
Title: Data-Lean Evolutionary Reinforcement Learning by Multitasking with Importance Sampling Abstract: Studies have shown evolution strategies (ES) to be a promising approach for reinforcement learning (RL) with deep neural networks. However, the issue of high sample complexity persists in applications of ES to deep R... |
Title: RGB-Depth Fusion GAN for Indoor Depth Completion Abstract: The raw depth image captured by the indoor depth sensor usually has an extensive range of missing depth values due to inherent limitations such as the inability to perceive transparent objects and limited distance range. The incomplete depth map burdens ... |
Title: Multi-class Label Noise Learning via Loss Decomposition and Centroid Estimation Abstract: In real-world scenarios, many large-scale datasets often contain inaccurate labels, i.e., noisy labels, which may confuse model training and lead to performance degradation. To overcome this issue, Label Noise Learning (LNL... |
Title: Efficient Algorithms for Extreme Bandits Abstract: In this paper, we contribute to the Extreme Bandit problem, a variant of Multi-Armed Bandits in which the learner seeks to collect the largest possible reward. We first study the concentration of the maximum of i.i.d random variables under mild assumptions on th... |
Title: Self-Imitation Learning from Demonstrations Abstract: Despite the numerous breakthroughs achieved with Reinforcement Learning (RL), solving environments with sparse rewards remains a challenging task that requires sophisticated exploration. Learning from Demonstrations (LfD) remedies this issue by guiding the ag... |
Title: Who Should Review Your Proposal? Interdisciplinary Topic Path Detection for Research Proposals Abstract: The peer merit review of research proposals has been the major mechanism to decide grant awards. Nowadays, research proposals have become increasingly interdisciplinary. It has been a longstanding challenge t... |
Title: TinyMLOps: Operational Challenges for Widespread Edge AI Adoption Abstract: Deploying machine learning applications on edge devices can bring clear benefits such as improved reliability, latency and privacy but it also introduces its own set of challenges. Most works focus on the limited computational resources ... |
Title: 3D Multi-Object Tracking Using Graph Neural Networks with Cross-Edge Modality Attention Abstract: Online 3D multi-object tracking (MOT) has witnessed significant research interest in recent years, largely driven by demand from the autonomous systems community. However, 3D offline MOT is relatively less explored.... |
Title: Transfer Dynamics in Emergent Evolutionary Curricula Abstract: PINSKY is a system for open-ended learning through neuroevolution in game-based domains. It builds on the Paired Open-Ended Trailblazer (POET) system, which originally explored learning and environment generation for bipedal walkers, and adapts it to... |
Title: Optimizing Trajectories for Highway Driving with Offline Reinforcement Learning Abstract: Implementing an autonomous vehicle that is able to output feasible, smooth and efficient trajectories is a long-standing challenge. Several approaches have been considered, roughly falling under two categories: rule-based a... |
Title: Active Meta-Learner for Log Analysis Abstract: The analysis of logs is a vital activity undertaken for cyber investigation, digital forensics and fault detection to enhance system and cyber resilience. However, performing log analysis is a complex task. It requires extensive knowledge of how the logs are generat... |
Title: Bike Sharing Demand Prediction based on Knowledge Sharing across Modes: A Graph-based Deep Learning Approach Abstract: Bike sharing is an increasingly popular part of urban transportation systems. Accurate demand prediction is the key to support timely re-balancing and ensure service efficiency. Most existing mo... |
Title: A Local Convergence Theory for the Stochastic Gradient Descent Method in Non-Convex Optimization With Non-isolated Local Minima Abstract: Loss functions with non-isolated minima have emerged in several machine learning problems, creating a gap between theory and practice. In this paper, we formulate a new type o... |
Title: GCF: Generalized Causal Forest for Heterogeneous Treatment Effect Estimation in Online Marketplace Abstract: Uplift modeling is a rapidly growing approach that utilizes machine learning and causal inference methods to estimate the heterogeneous treatment effects. It has been widely adopted and applied to online ... |
Title: BNS-GCN: Efficient Full-Graph Training of Graph Convolutional Networks with Partition-Parallelism and Random Boundary Node Sampling Abstract: Graph Convolutional Networks (GCNs) have emerged as the state-of-the-art method for graph-based learning tasks. However, training GCNs at scale is still challenging, hinde... |
Title: Hierarchical autoregressive neural networks for statistical systems Abstract: It was recently proposed that neural networks could be used to approximate many-dimensional probability distributions that appear e.g. in lattice field theories or statistical mechanics. Subsequently they can be used as variational app... |
Title: Optimal Fine-Grained N:M sparsity for Activations and Neural Gradients Abstract: In deep learning, fine-grained N:M sparsity reduces the data footprint and bandwidth of a General Matrix multiply (GEMM) by x2, and doubles throughput by skipping computation of zero values. So far, it was only used to prune weights... |
Title: Online Skeleton-based Action Recognition with Continual Spatio-Temporal Graph Convolutional Networks Abstract: Graph-based reasoning over skeleton data has emerged as a promising approach for human action recognition. However, the application of prior graph-based methods, which predominantly employ whole tempora... |
Title: Reinforced MOOCs Concept Recommendation in Heterogeneous Information Networks Abstract: Massive open online courses (MOOCs), which provide a large-scale interactive participation and open access via the web, are becoming a modish way for online and distance education. To help users have a better study experience... |
Title: Learning Resilient Radio Resource Management Policies with Graph Neural Networks Abstract: We consider the problems of downlink user selection and power control in wireless networks, comprising multiple transmitters and receivers communicating with each other over a shared wireless medium. To achieve a high aggr... |
Title: DHEN: A Deep and Hierarchical Ensemble Network for Large-Scale Click-Through Rate Prediction Abstract: Learning feature interactions is important to the model performance of online advertising services. As a result, extensive efforts have been devoted to designing effective architectures to learn feature interac... |
Title: Filter Drug-induced Liver Injury Literature with Natural Language Processing and Ensemble Learning Abstract: Drug-induced liver injury (DILI) describes the adverse effects of drugs that damage liver. Life-threatening results including liver failure or death were also reported in severe DILI cases. Therefore, DIL... |
Title: Multigrid-augmented deep learning preconditioners for the Helmholtz equation Abstract: In this paper, we present a data-driven approach to iteratively solve the discrete heterogeneous Helmholtz equation at high wavenumbers. In our approach, we combine classical iterative solvers with convolutional neural network... |
Title: An Introduction to Matrix factorization and Factorization Machines in Recommendation System, and Beyond Abstract: This paper aims at a better understanding of matrix factorization (MF), factorization machines (FM), and their combination with deep algorithms' application in recommendation systems. Specifically, t... |
Title: A new perspective on probabilistic image modeling Abstract: We present the Deep Convolutional Gaussian Mixture Model (DCGMM), a new probabilistic approach for image modeling capable of density estimation, sampling and tractable inference. DCGMM instances exhibit a CNN-like layered structure, in which the princip... |
Title: Differentiable Duration Modeling for End-to-End Text-to-Speech Abstract: Parallel text-to-speech (TTS) models have recently enabled fast and highly-natural speech synthesis. However, such models typically require external alignment models, which are not necessarily optimized for the decoder as they are not joint... |
Title: Optimizing Revenue Maximization and Demand Learning in Airline Revenue Management Abstract: Correctly estimating how demand respond to prices is fundamental for airlines willing to optimize their pricing policy. Under some conditions, these policies, while aiming at maximizing short term revenue, can present too... |
Title: From Concept Drift to Model Degradation: An Overview on Performance-Aware Drift Detectors Abstract: The dynamicity of real-world systems poses a significant challenge to deployed predictive machine learning (ML) models. Changes in the system on which the ML model has been trained may lead to performance degradat... |
Title: Collaborative Learning for Cyberattack Detection in Blockchain Networks Abstract: This article aims to study intrusion attacks and then develop a novel cyberattack detection framework for blockchain networks. Specifically, we first design and implement a blockchain network in our laboratory. This blockchain netw... |
Title: Telling Stories from Computational Notebooks: AI-Assisted Presentation Slides Creation for Presenting Data Science Work Abstract: Creating presentation slides is a critical but time-consuming task for data scientists. While researchers have proposed many AI techniques to lift data scientists' burden on data prep... |
Title: Overcoming Oscillations in Quantization-Aware Training Abstract: When training neural networks with simulated quantization, we observe that quantized weights can, rather unexpectedly, oscillate between two grid-points. The importance of this effect and its impact on quantization-aware training are not well-under... |
Title: Ovid: A Machine Learning Approach for Automated Vandalism Detection in OpenStreetMap Abstract: OpenStreetMap is a unique source of openly available worldwide map data, increasingly adopted in real-world applications. Vandalism detection in OpenStreetMap is critical and remarkably challenging due to the large sca... |
Title: GCNET: graph-based prediction of stock price movement using graph convolutional network Abstract: The prediction of stocks' direction of movement using the historical price information has attracted considerable attention as a challenging problem in the field of machine learning. However, modeling and analyzing ... |
Title: Diverse Counterfactual Explanations for Anomaly Detection in Time Series Abstract: Data-driven methods that detect anomalies in times series data are ubiquitous in practice, but they are in general unable to provide helpful explanations for the predictions they make. In this work we propose a model-agnostic algo... |
Title: FGAN: Federated Generative Adversarial Networks for Anomaly Detection in Network Traffic Abstract: Over the last two decades, a lot of work has been done in improving network security, particularly in intrusion detection systems (IDS) and anomaly detection. Machine learning solutions have also been employed in I... |
Title: No Pain, Big Gain: Classify Dynamic Point Cloud Sequences with Static Models by Fitting Feature-level Space-time Surfaces Abstract: Scene flow is a powerful tool for capturing the motion field of 3D point clouds. However, it is difficult to directly apply flow-based models to dynamic point cloud classification s... |
Title: PACS: A Dataset for Physical Audiovisual CommonSense Reasoning Abstract: In order for AI to be safely deployed in real-world scenarios such as hospitals, schools, and the workplace, they should be able to reason about the physical world by understanding the physical properties and affordances of available object... |
Title: Towards Explainable Evaluation Metrics for Natural Language Generation Abstract: Unlike classical lexical overlap metrics such as BLEU, most current evaluation metrics (such as BERTScore or MoverScore) are based on black-box language models such as BERT or XLM-R. They often achieve strong correlations with human... |
Title: Review of Disentanglement Approaches for Medical Applications -- Towards Solving the Gordian Knot of Generative Models in Healthcare Abstract: Deep neural networks are commonly used for medical purposes such as image generation, segmentation, or classification. Besides this, they are often criticized as black bo... |
Title: Can we integrate spatial verification methods into neural-network loss functions for atmospheric science? Abstract: In the last decade, much work in atmospheric science has focused on spatial verification (SV) methods for gridded prediction, which overcome serious disadvantages of pixelwise verification. However... |
Title: Multispectral Satellite Data Classification using Soft Computing Approach Abstract: A satellite image is a remotely sensed image data, where each pixel represents a specific location on earth. The pixel value recorded is the reflection radiation from the earth's surface at that location. Multispectral images are... |
Title: Teaching language models to support answers with verified quotes Abstract: Recent large language models often answer factual questions correctly. But users can't trust any given claim a model makes without fact-checking, because language models can hallucinate convincing nonsense. In this work we use reinforceme... |
Title: Prediction of chaotic attractors in quasiperiodically forced logistic map using deep learning Abstract: We forecast two different chaotic dynamics of the quasiperiodically forced logistic map using the well-known deep learning framework Long Short-Term Memory. We generate two data sets and use one in the trainin... |
Title: Short Text Topic Modeling: Application to tweets about Bitcoin Abstract: Understanding the semantic of a collection of texts is a challenging task. Topic models are probabilistic models that aims at extracting "topics" from a corpus of documents. This task is particularly difficult when the corpus is composed of... |
Title: Application of Quantum Density Matrix in Classical Question Answering and Classical Image Classification Abstract: Quantum density matrix represents all the information of the entire quantum system, and novel models of meaning employing density matrices naturally model linguistic phenomena such as hyponymy and l... |
Title: Operator Sketching for Deep Unrolling Networks Abstract: In this work we propose a new paradigm for designing efficient deep unrolling networks using operator sketching. The deep unrolling networks are currently the state-of-the-art solutions for imaging inverse problems. However, for high-dimensional imaging ta... |
Title: Force-matching Coarse-Graining without Forces Abstract: Coarse-grained (CG) molecular simulations have become a standard tool to study molecular processes on time-~and length-scales inaccessible to all-atom simulations. Learning CG force fields from all-atom data has mainly relied on force-matching and relative ... |
Title: One After Another: Learning Incremental Skills for a Changing World Abstract: Reward-free, unsupervised discovery of skills is an attractive alternative to the bottleneck of hand-designing rewards in environments where task supervision is scarce or expensive. However, current skill pre-training methods, like man... |
Title: Physics-driven Synthetic Data Learning for Biomedical Magnetic Resonance Abstract: Deep learning has innovated the field of computational imaging. One of its bottlenecks is unavailable or insufficient training data. This article reviews an emerging paradigm, imaging physics-based data synthesis (IPADS), that can... |
Title: Generating Fast and Slow: Scene Decomposition via Reconstruction Abstract: We consider the problem of segmenting scenes into constituent entities, i.e. underlying objects and their parts. Current supervised visual detectors though impressive within their training distribution, often fail to segment out-of-distri... |
Title: Performance of Deep Learning models with transfer learning for multiple-step-ahead forecasts in monthly time series Abstract: Deep Learning and transfer learning models are being used to generate time series forecasts; however, there is scarce evidence about their performance prediction that it is more evident f... |
Title: Teachable Reinforcement Learning via Advice Distillation Abstract: Training automated agents to complete complex tasks in interactive environments is challenging: reinforcement learning requires careful hand-engineering of reward functions, imitation learning requires specialized infrastructure and access to a h... |
Title: Distinguishing Non-natural from Natural Adversarial Samples for More Robust Pre-trained Language Model Abstract: Recently, the problem of robustness of pre-trained language models (PrLMs) has received increasing research interest. Latest studies on adversarial attacks achieve high attack success rates against Pr... |
Title: Exploiting Neighbor Effect: Conv-Agnostic GNNs Framework for Graphs with Heterophily Abstract: Due to the homophily assumption in graph convolution networks, a common consensus is that graph neural networks (GNNs) perform well on homophilic graphs but may fail on heterophilic graphs with many inter-class edges. ... |
Title: Efficient Neural Network Analysis with Sum-of-Infeasibilities Abstract: Inspired by sum-of-infeasibilities methods in convex optimization, we propose a novel procedure for analyzing verification queries on neural networks with piecewise-linear activation functions. Given a convex relaxation which over-approximat... |
Title: Reinforcement learning for automatic quadrilateral mesh generation: a soft actor-critic approach Abstract: This paper proposes, implements, and evaluates a Reinforcement Learning (RL) based computational framework for automatic mesh generation. Mesh generation, as one of six basic research directions identified ... |
Title: Hybrid training of optical neural networks Abstract: Optical neural networks are emerging as a promising type of machine learning hardware capable of energy-efficient, parallel computation. Today's optical neural networks are mainly developed to perform optical inference after in silico training on digital simul... |
Title: On the Effect of Pre-Processing and Model Complexity for Plastic Analysis Using Short-Wave-Infrared Hyper-Spectral Imaging Abstract: The importance of plastic waste recycling is undeniable. In this respect, computer vision and deep learning enable solutions through the automated analysis of short-wave-infrared h... |
Title: ReCCoVER: Detecting Causal Confusion for Explainable Reinforcement Learning Abstract: Despite notable results in various fields over the recent years, deep reinforcement learning (DRL) algorithms lack transparency, affecting user trust and hindering their deployment to high-risk tasks. Causal confusion refers to... |
Title: The Conceptual VAE Abstract: In this report we present a new model of concepts, based on the framework of variational autoencoders, which is designed to have attractive properties such as factored conceptual domains, and at the same time be learnable from data. The model is inspired by, and closely related to, t... |
Title: A survey on GANs for computer vision: Recent research, analysis and taxonomy Abstract: In the last few years, there have been several revolutions in the field of deep learning, mainly headlined by the large impact of Generative Adversarial Networks (GANs). GANs not only provide an unique architecture when defini... |
Title: Healthy Twitter discussions? Time will tell Abstract: Studying misinformation and how to deal with unhealthy behaviours within online discussions has recently become an important field of research within social studies. With the rapid development of social media, and the increasing amount of available informatio... |
Title: Domain Knowledge Aids in Signal Disaggregation; the Example of the Cumulative Water Heater Abstract: In this article we present an unsupervised low-frequency method aimed at detecting and disaggregating the power used by Cumulative Water Heaters (CWH) in residential homes. Our model circumvents the inherent diff... |
Title: Model Comparison in Approximate Bayesian Computation Abstract: A common problem in natural sciences is the comparison of competing models in the light of observed data. Bayesian model comparison provides a statistically sound framework for this comparison based on the evidence each model provides for the data. H... |
Title: One-Bit Compressive Sensing: Can We Go Deep and Blind? Abstract: One-bit compressive sensing is concerned with the accurate recovery of an underlying sparse signal of interest from its one-bit noisy measurements. The conventional signal recovery approaches for this problem are mainly developed based on the assum... |
Title: EEG based Emotion Recognition: A Tutorial and Review Abstract: Emotion recognition technology through analyzing the EEG signal is currently an essential concept in Artificial Intelligence and holds great potential in emotional health care, human-computer interaction, multimedia content recommendation, etc. Thoug... |
Title: A Contrastive Objective for Learning Disentangled Representations Abstract: Learning representations of images that are invariant to sensitive or unwanted attributes is important for many tasks including bias removal and cross domain retrieval. Here, our objective is to learn representations that are invariant t... |
Title: PCA-RF: An Efficient Parkinson's Disease Prediction Model based on Random Forest Classification Abstract: In this modern era of overpopulation disease prediction is a crucial step in diagnosing various diseases at an early stage. With the advancement of various machine learning algorithms, the prediction has bec... |
Title: Benchmarking Test-Time Unsupervised Deep Neural Network Adaptation on Edge Devices Abstract: The prediction accuracy of the deep neural networks (DNNs) after deployment at the edge can suffer with time due to shifts in the distribution of the new data. To improve robustness of DNNs, they must be able to update t... |
Title: Landscape Analysis for Surrogate Models in the Evolutionary Black-Box Context Abstract: Surrogate modeling has become a valuable technique for black-box optimization tasks with expensive evaluation of the objective function. In this paper, we investigate the relationship between the predictive accuracy of surrog... |
Title: Random vector functional link network: recent developments, applications, and future directions Abstract: Neural networks have been successfully employed in various domain such as classification, regression and clustering, etc. Generally, the back propagation (BP) based iterative approaches are used to train the... |
Title: The Change that Matters in Discourse Parsing: Estimating the Impact of Domain Shift on Parser Error Abstract: Discourse analysis allows us to attain inferences of a text document that extend beyond the sentence-level. The current performance of discourse models is very low on texts outside of the training distri... |
Title: Deep Reinforcement Learning and Convex Mean-Variance Optimisation for Portfolio Management Abstract: Traditional portfolio management methods can incorporate specific investor preferences but rely on accurate forecasts of asset returns and covariances. Reinforcement learning (RL) methods do not rely on these exp... |
Title: Alarm-Based Root Cause Analysis in Industrial Processes Using Deep Learning Abstract: Alarm management systems have become indispensable in modern industry. Alarms inform the operator of abnormal situations, particularly in the case of equipment failures. Due to the interconnections between various parts of the ... |
Title: Training Quantised Neural Networks with STE Variants: the Additive Noise Annealing Algorithm Abstract: Training quantised neural networks (QNNs) is a non-differentiable optimisation problem since weights and features are output by piecewise constant functions. The standard solution is to apply the straight-throu... |
Title: Learning robot motor skills with mixed reality Abstract: Mixed Reality (MR) has recently shown great success as an intuitive interface for enabling end-users to teach robots. Related works have used MR interfaces to communicate robot intents and beliefs to a co-located human, as well as developed algorithms for ... |
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