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Title: Federated Remote Physiological Measurement with Imperfect Data Abstract: The growing need for technology that supports remote healthcare is being acutely highlighted by an aging population and the COVID-19 pandemic. In health-related machine learning applications the ability to learn predictive models without da... |
Title: Dual reparametrized Variational Generative Model for Time-Series Forecasting Abstract: This paper propose DualVDT, a generative model for Time-series forecasting. Introduced dual reparametrized variational mechanisms on variational autoencoder (VAE) to tighter the evidence lower bound (ELBO) of the model, prove ... |
Title: Research on Parallel SVM Algorithm Based on Cascade SVM Abstract: Cascade SVM (CSVM) can group datasets and train subsets in parallel, which greatly reduces the training time and memory consumption. However, the model accuracy obtained by using this method has some errors compared with direct training. In order ... |
Title: Reinforcement Learning for Linear Quadratic Control is Vulnerable Under Cost Manipulation Abstract: In this work, we study the deception of a Linear-Quadratic-Gaussian (LQG) agent by manipulating the cost signals. We show that a small falsification of the cost parameters will only lead to a bounded change in the... |
Title: Acoustic To Articulatory Speech Inversion Using Multi-Resolution Spectro-Temporal Representations Of Speech Signals Abstract: Multi-resolution spectro-temporal features of a speech signal represent how the brain perceives sounds by tuning cortical cells to different spectral and temporal modulations. These featu... |
Title: Overcoming Temptation: Incentive Design For Intertemporal Choice Abstract: Individuals are often faced with temptations that can lead them astray from long-term goals. We're interested in developing interventions that steer individuals toward making good initial decisions and then maintaining those decisions ove... |
Title: FLAG: Flow-based 3D Avatar Generation from Sparse Observations Abstract: To represent people in mixed reality applications for collaboration and communication, we need to generate realistic and faithful avatar poses. However, the signal streams that can be applied for this task from head-mounted devices (HMDs) a... |
Title: PathSAGE: Spatial Graph Attention Neural Networks With Random Path Sampling Abstract: Graph Convolutional Networks (GCNs) achieve great success in non-Euclidean structure data processing recently. In existing studies, deeper layers are used in CCNs to extract deeper features of Euclidean structure data. However,... |
Title: Near-optimal Offline Reinforcement Learning with Linear Representation: Leveraging Variance Information with Pessimism Abstract: Offline reinforcement learning, which seeks to utilize offline/historical data to optimize sequential decision-making strategies, has gained surging prominence in recent studies. Due t... |
Title: Reprogramming FairGANs with Variational Auto-Encoders: A New Transfer Learning Model Abstract: Fairness-aware GANs (FairGANs) exploit the mechanisms of Generative Adversarial Networks (GANs) to impose fairness on the generated data, freeing them from both disparate impact and disparate treatment. Given the model... |
Title: Averaging Spatio-temporal Signals using Optimal Transport and Soft Alignments Abstract: Several fields in science, from genomics to neuroimaging, require monitoring populations (measures) that evolve with time. These complex datasets, describing dynamics with both time and spatial components, pose new challenges... |
Title: No Free Lunch Theorem for Security and Utility in Federated Learning Abstract: In a federated learning scenario where multiple parties jointly learn a model from their respective data, there exist two conflicting goals for the choice of appropriate algorithms. On one hand, private and sensitive training data mus... |
Title: ZIN: When and How to Learn Invariance by Environment Inference? Abstract: It is commonplace to encounter heterogeneous data, of which some aspects of the data distribution may vary but the underlying causal mechanisms remain constant. When data are divided into distinct environments according to the heterogeneit... |
Title: MLRM: A Multiple Linear Regression based Model for Average Temperature Prediction of A Day Abstract: Weather is a phenomenon that affects everything and everyone around us on a daily basis. Weather prediction has been an important point of study for decades as researchers have tried to predict the weather and cl... |
Title: Multiple Inputs Neural Networks for Medicare fraud Detection Abstract: Medicare fraud results in considerable losses for governments and insurance companies and results in higher premiums from clients. Medicare fraud costs around 13 billion euros in Europe and between 21 billion and 71 billion US dollars per yea... |
Title: Flexible Amortized Variational Inference in qBOLD MRI Abstract: Streamlined qBOLD acquisitions enable experimentally straightforward observations of brain oxygen metabolism. $R_2^\prime$ maps are easily inferred; however, the Oxygen extraction fraction (OEF) and deoxygenated blood volume (DBV) are more ambiguous... |
Title: Wireless Quantized Federated Learning: A Joint Computation and Communication Design Abstract: Recently, federated learning (FL) has sparked widespread attention as a promising decentralized machine learning approach which provides privacy and low delay. However, communication bottleneck still constitutes an issu... |
Title: Multi-modal Graph Learning for Disease Prediction Abstract: Benefiting from the powerful expressive capability of graphs, graph-based approaches have been popularly applied to handle multi-modal medical data and achieved impressive performance in various biomedical applications. For disease prediction tasks, mos... |
Title: Identifiability of Causal-based Fairness Notions: A State of the Art Abstract: Machine learning algorithms can produce biased outcome/prediction, typically, against minorities and under-represented sub-populations. Therefore, fairness is emerging as an important requirement for the large scale application of mac... |
Title: Cross-Layer Approximation For Printed Machine Learning Circuits Abstract: Printed electronics (PE) feature low non-recurring engineering costs and low per unit-area fabrication costs, enabling thus extremely low-cost and on-demand hardware. Such low-cost fabrication allows for high customization that would be in... |
Title: Integrating Dependency Tree Into Self-attention for Sentence Representation Abstract: Recent progress on parse tree encoder for sentence representation learning is notable. However, these works mainly encode tree structures recursively, which is not conducive to parallelization. On the other hand, these works ra... |
Title: Graph Summarization with Graph Neural Networks Abstract: The goal of graph summarization is to represent large graphs in a structured and compact way. A graph summary based on equivalence classes preserves pre-defined features of a graph's vertex within a $k$-hop neighborhood such as the vertex labels and edge l... |
Title: Generalized Bandit Regret Minimizer Framework in Imperfect Information Extensive-Form Game Abstract: Regret minimization methods are a powerful tool for learning approximate Nash equilibrium (NE) in two-player zero-sum imperfect information extensive-form games (IIEGs). We consider the problem in the interactive... |
Title: FedSyn: Synthetic Data Generation using Federated Learning Abstract: As Deep Learning algorithms continue to evolve and become more sophisticated, they require massive datasets for model training and efficacy of models. Some of those data requirements can be met with the help of existing datasets within the orga... |
Title: Are discrete units necessary for Spoken Language Modeling? Abstract: Recent work in spoken language modeling shows the possibility of learning a language unsupervisedly from raw audio without any text labels. The approach relies first on transforming the audio into a sequence of discrete units (or pseudo-text) a... |
Title: Hybrid Artifact Detection System for Minute Resolution Blood Pressure Signals from ICU Abstract: Physiological monitoring in intensive care units generates data that can be used to aid clinical decision making facilitating early interventions. However, the low data quality of physiological signals due to the rec... |
Title: Block-Sparse Adversarial Attack to Fool Transformer-Based Text Classifiers Abstract: Recently, it has been shown that, in spite of the significant performance of deep neural networks in different fields, those are vulnerable to adversarial examples. In this paper, we propose a gradient-based adversarial attack a... |
Title: Label-efficient Hybrid-supervised Learning for Medical Image Segmentation Abstract: Due to the lack of expertise for medical image annotation, the investigation of label-efficient methodology for medical image segmentation becomes a heated topic. Recent progresses focus on the efficient utilization of weak annot... |
Title: Policy Architectures for Compositional Generalization in Control Abstract: Many tasks in control, robotics, and planning can be specified using desired goal configurations for various entities in the environment. Learning goal-conditioned policies is a natural paradigm to solve such tasks. However, current appro... |
Title: Random Ensemble Reinforcement Learning for Traffic Signal Control Abstract: Traffic signal control is a significant part of the construction of intelligent transportation. An efficient traffic signal control strategy can reduce traffic congestion, improve urban road traffic efficiency and facilitate people's liv... |
Title: Anti-Oversmoothing in Deep Vision Transformers via the Fourier Domain Analysis: From Theory to Practice Abstract: Vision Transformer (ViT) has recently demonstrated promise in computer vision problems. However, unlike Convolutional Neural Networks (CNN), it is known that the performance of ViT saturates quickly ... |
Title: Online User Profiling to Detect Social Bots on Twitter Abstract: Social media platforms can expose influential trends in many aspects of everyday life. However, the movements they represent can be contaminated by disinformation. Social bots are one of the significant sources of disinformation in social media. So... |
Title: Multi-Channel Convolutional Analysis Operator Learning for Dual-Energy CT Reconstruction Abstract: Objective. Dual-energy computed tomography (DECT) has the potential to improve contrast, reduce artifacts and the ability to perform material decomposition in advanced imaging applications. The increased number or ... |
Title: Imitation and Adaptation Based on Consistency: A Quadruped Robot Imitates Animals from Videos Using Deep Reinforcement Learning Abstract: The essence of quadrupeds' movements is the movement of the center of gravity, which has a pattern in the action of quadrupeds. However, the gait motion planning of the quadru... |
Title: Graph Neural Networks for Relational Inductive Bias in Vision-based Deep Reinforcement Learning of Robot Control Abstract: State-of-the-art reinforcement learning algorithms predominantly learn a policy from either a numerical state vector or images. Both approaches generally do not take structural knowledge of ... |
Title: Polar Transformation Based Multiple Instance Learning Assisting Weakly Supervised Image Segmentation With Loose Bounding Box Annotations Abstract: This study investigates weakly supervised image segmentation using loose bounding box supervision. It presents a multiple instance learning strategy based on polar tr... |
Title: Enhancing Adversarial Training with Second-Order Statistics of Weights Abstract: Adversarial training has been shown to be one of the most effective approaches to improve the robustness of deep neural networks. It is formalized as a min-max optimization over model weights and adversarial perturbations, where the... |
Title: The Role of ImageNet Classes in Fr\'echet Inception Distance Abstract: Fr\'echet Inception Distance (FID) is a metric for quantifying the distance between two distributions of images. Given its status as a standard yardstick for ranking models in data-driven generative modeling research, it seems important that ... |
Title: Exploiting Low-Rank Tensor-Train Deep Neural Networks Based on Riemannian Gradient Descent With Illustrations of Speech Processing Abstract: This work focuses on designing low complexity hybrid tensor networks by considering trade-offs between the model complexity and practical performance. Firstly, we exploit a... |
Title: The Long Arc of Fairness: Formalisations and Ethical Discourse Abstract: In recent years, the idea of formalising and modelling fairness for algorithmic decision making (ADM) has advanced to a point of sophisticated specialisation. However, the relations between technical (formalised) and ethical discourse on fa... |
Title: Embedding Earth: Self-supervised contrastive pre-training for dense land cover classification Abstract: In training machine learning models for land cover semantic segmentation there is a stark contrast between the availability of satellite imagery to be used as inputs and ground truth data to enable supervised ... |
Title: Sparse Subspace Clustering for Concept Discovery (SSCCD) Abstract: Concepts are key building blocks of higher level human understanding. Explainable AI (XAI) methods have shown tremendous progress in recent years, however, local attribution methods do not allow to identify coherent model behavior across samples ... |
Title: Universally Consistent Online Learning with Arbitrarily Dependent Responses Abstract: This work provides an online learning rule that is universally consistent under processes on (X,Y) pairs, under conditions only on the X process. As a special case, the conditions admit all processes on (X,Y) such that the proc... |
Title: A Machine Learning Approach for Prosumer Management in Intraday Electricity Markets Abstract: Prosumer operators are dealing with extensive challenges to participate in short-term electricity markets while taking uncertainties into account. Challenges such as variation in demand, solar energy, wind power, and el... |
Title: Active Evaluation: Efficient NLG Evaluation with Few Pairwise Comparisons Abstract: Recent studies have shown the advantages of evaluating NLG systems using pairwise comparisons as opposed to direct assessment. Given $k$ systems, a naive approach for identifying the top-ranked system would be to uniformly obtain... |
Title: Climate Change & Computer Audition: A Call to Action and Overview on Audio Intelligence to Help Save the Planet Abstract: Among the seventeen Sustainable Development Goals (SDGs) proposed within the 2030 Agenda and adopted by all the United Nations member states, the 13$^{th}$ SDG is a call for action to combat ... |
Title: Econometric Modeling of Intraday Electricity Market Price with Inadequate Historical Data Abstract: The intraday (ID) electricity market has received an increasing attention in the recent EU electricity-market discussions. This is partly because the uncertainty in the underlying power system is growing and the I... |
Title: WLASL-LEX: a Dataset for Recognising Phonological Properties in American Sign Language Abstract: Signed Language Processing (SLP) concerns the automated processing of signed languages, the main means of communication of Deaf and hearing impaired individuals. SLP features many different tasks, ranging from sign r... |
Title: On the Difficulty of Epistemic Uncertainty Quantification in Machine Learning: The Case of Direct Uncertainty Estimation through Loss Minimisation Abstract: Uncertainty quantification has received increasing attention in machine learning in the recent past. In particular, a distinction between aleatoric and epis... |
Title: Multi-sensor large-scale dataset for multi-view 3D reconstruction Abstract: We present a new multi-sensor dataset for 3D surface reconstruction. It includes registered RGB and depth data from sensors of different resolutions and modalities: smartphones, Intel RealSense, Microsoft Kinect, industrial cameras, and ... |
Title: Detection of multiple retinal diseases in ultra-widefield fundus images using deep learning: data-driven identification of relevant regions Abstract: Ultra-widefield (UWF) imaging is a promising modality that captures a larger retinal field of view compared to traditional fundus photography. Previous studies sho... |
Title: GATSPI: GPU Accelerated Gate-Level Simulation for Power Improvement Abstract: In this paper, we present GATSPI, a novel GPU accelerated logic gate simulator that enables ultra-fast power estimation for industry sized ASIC designs with millions of gates. GATSPI is written in PyTorch with custom CUDA kernels for e... |
Title: An Empirical Study of Graphormer on Large-Scale Molecular Modeling Datasets Abstract: This technical note describes the recent updates of Graphormer, including architecture design modifications, and the adaption to 3D molecular dynamics simulation. The "Graphormer-V2" could attain better results on large-scale m... |
Title: Protein Representation Learning by Geometric Structure Pretraining Abstract: Learning effective protein representations is critical in a variety of tasks in biology such as predicting protein function or structure. Existing approaches usually pretrain protein language models on a large number of unlabeled amino ... |
Title: Symmetry Group Equivariant Architectures for Physics Abstract: Physical theories grounded in mathematical symmetries are an essential component of our understanding of a wide range of properties of the universe. Similarly, in the domain of machine learning, an awareness of symmetries such as rotation or permutat... |
Title: Personalized Execution Time Optimization for the Scheduled Jobs Abstract: Scheduled batch jobs have been widely used on the asynchronous computing platforms to execute various enterprise applications, including the scheduled notifications and the candidate computation for the modern recommender systems. It is im... |
Title: LaPraDoR: Unsupervised Pretrained Dense Retriever for Zero-Shot Text Retrieval Abstract: In this paper, we propose LaPraDoR, a pretrained dual-tower dense retriever that does not require any supervised data for training. Specifically, we first present Iterative Contrastive Learning (ICoL) that iteratively trains... |
Title: Masked Visual Pre-training for Motor Control Abstract: This paper shows that self-supervised visual pre-training from real-world images is effective for learning motor control tasks from pixels. We first train the visual representations by masked modeling of natural images. We then freeze the visual encoder and ... |
Title: More Than a Toy: Random Matrix Models Predict How Real-World Neural Representations Generalize Abstract: Of theories for why large-scale machine learning models generalize despite being vastly overparameterized, which of their assumptions are needed to capture the qualitative phenomena of generalization in the r... |
Title: Tactile-ViewGCN: Learning Shape Descriptor from Tactile Data using Graph Convolutional Network Abstract: For humans, our "senses of touch" have always been necessary for our ability to precisely and efficiently manipulate objects of all shapes in any environment, but until recently, not many works have been done... |
Title: Leveraging universality of jet taggers through transfer learning Abstract: A significant challenge in the tagging of boosted objects via machine-learning technology is the prohibitive computational cost associated with training sophisticated models. Nevertheless, the universality of QCD suggests that a large amo... |
Title: TrafPS: A Visual Analysis System Interpreting Traffic Prediction in Shapley Abstract: In recent years, deep learning approaches have been proved good performance in traffic flow prediction, many complex models have been proposed to make traffic flow prediction more accurate. However, lacking transparency limits ... |
Title: Learning cardiac activation maps from 12-lead ECG with multi-fidelity Bayesian optimization on manifolds Abstract: We propose a method for identifying an ectopic activation in the heart non-invasively. Ectopic activity in the heart can trigger deadly arrhythmias. The localization of the ectopic foci or earliest ... |
Title: Generalized Key-Value Memory to Flexibly Adjust Redundancy in Memory-Augmented Networks Abstract: Memory-augmented neural networks enhance a neural network with an external key-value memory whose complexity is typically dominated by the number of support vectors in the key memory. We propose a generalized key-va... |
Title: verBERT: Automating Brazilian Case Law Document Multi-label Categorization Using BERT Abstract: In this work, we carried out a study about the use of attention-based algorithms to automate the categorization of Brazilian case law documents. We used data from the Kollemata Project to produce two distinct datasets... |
Title: Sampling Bias Correction for Supervised Machine Learning: A Bayesian Inference Approach with Practical Applications Abstract: Given a supervised machine learning problem where the training set has been subject to a known sampling bias, how can a model be trained to fit the original dataset? We achieve this throu... |
Title: AI agents for facilitating social interactions and wellbeing Abstract: Wellbeing AI has been becoming a new trend in individuals' mental health, organizational health, and flourishing our societies. Various applications of wellbeing AI have been introduced to our daily lives. While social relationships within gr... |
Title: Pressure Ulcer Categorisation using Deep Learning: A Clinical Trial to Evaluate Model Performance Abstract: Pressure ulcers are a challenge for patients and healthcare professionals. In the UK, 700,000 people are affected by pressure ulcers each year. Treating them costs the National Health Service {\pounds}3.8 ... |
Title: Learning from humans: combining imitation and deep reinforcement learning to accomplish human-level performance on a virtual foraging task Abstract: We develop a method to learn bio-inspired foraging policies using human data. We conduct an experiment where humans are virtually immersed in an open field foraging... |
Title: Preliminary experiments on automatic gender recognition based on online capital letters Abstract: In this paper we present some experiments to automatically classify online handwritten text based on capital letters. Although handwritten text is not as discriminative as face or voice, we still found some chance f... |
Title: Parameter Inference of Time Series by Delay Embeddings and Learning Differentiable Operators Abstract: A common issue in dealing with real-world dynamical systems is identifying system parameters responsible for its behavior. A frequent scenario is that one has time series data, along with corresponding paramete... |
Title: Bit-Metric Decoding Rate in Multi-User MIMO Systems: Theory Abstract: Link-adaptation (LA) is one of the most important aspects of wireless communications where the modulation and coding scheme (MCS) used by the transmitter is adapted to the channel conditions in order to meet a certain target error-rate. In a s... |
Title: Bit-Metric Decoding Rate in Multi-User MIMO Systems: Applications Abstract: This is the second part of a two-part paper that focuses on link-adaptation (LA) and physical layer (PHY) abstraction for multi-user MIMO (MU-MIMO) systems with non-linear receivers. The first part proposes a new metric, called bit-metri... |
Title: SOCKS: A Stochastic Optimal Control and Reachability Toolbox Using Kernel Methods Abstract: We present SOCKS, a data-driven stochastic optimal control toolbox based in kernel methods. SOCKS is a collection of data-driven algorithms that compute approximate solutions to stochastic optimal control problems with ar... |
Title: Instance-Dependent Regret Analysis of Kernelized Bandits Abstract: We study the kernelized bandit problem, that involves designing an adaptive strategy for querying a noisy zeroth-order-oracle to efficiently learn about the optimizer of an unknown function $f$ with a norm bounded by $M<\infty$ in a Reproducing K... |
Title: The Principle of Diversity: Training Stronger Vision Transformers Calls for Reducing All Levels of Redundancy Abstract: Vision transformers (ViTs) have gained increasing popularity as they are commonly believed to own higher modeling capacity and representation flexibility, than traditional convolutional network... |
Title: Combining Deep Learning with Physics Based Features in Explosion-Earthquake Discrimination Abstract: This paper combines the power of deep-learning with the generalizability of physics-based features, to present an advanced method for seismic discrimination between earthquakes and explosions. The proposed method... |
Title: The Health Gym: Synthetic Health-Related Datasets for the Development of Reinforcement Learning Algorithms Abstract: In recent years, the machine learning research community has benefited tremendously from the availability of openly accessible benchmark datasets. Clinical data are usually not openly available du... |
Title: Varying Coefficient Linear Discriminant Analysis for Dynamic Data Abstract: Linear discriminant analysis (LDA) is a vital classification tool in statistics and machine learning. This paper investigates the varying coefficient LDA model for dynamic data, with Bayes' discriminant direction being a function of some... |
Title: GRAND+: Scalable Graph Random Neural Networks Abstract: Graph neural networks (GNNs) have been widely adopted for semi-supervised learning on graphs. A recent study shows that the graph random neural network (GRAND) model can generate state-of-the-art performance for this problem. However, it is difficult for GR... |
Title: A Proposal to Study "Is High Quality Data All We Need?" Abstract: Even though deep neural models have achieved superhuman performance on many popular benchmarks, they have failed to generalize to OOD or adversarial datasets. Conventional approaches aimed at increasing robustness include developing increasingly l... |
Title: Concentration Network for Reinforcement Learning of Large-Scale Multi-Agent Systems Abstract: When dealing with a series of imminent issues, humans can naturally concentrate on a subset of these concerning issues by prioritizing them according to their contributions to motivational indices, e.g., the probability... |
Title: Categories of Differentiable Polynomial Circuits for Machine Learning Abstract: Reverse derivative categories (RDCs) have recently been shown to be a suitable semantic framework for studying machine learning algorithms. Whereas emphasis has been put on training methodologies, less attention has been devoted to p... |
Title: Deep learning-based conditional inpainting for restoration of artifact-affected 4D CT images Abstract: 4D CT imaging is an essential component of radiotherapy of thoracic/abdominal tumors. 4D CT images are, however, often affected by artifacts that compromise treatment planning quality. In this work, deep learni... |
Title: Equivariant Graph Mechanics Networks with Constraints Abstract: Learning to reason about relations and dynamics over multiple interacting objects is a challenging topic in machine learning. The challenges mainly stem from that the interacting systems are exponentially-compositional, symmetrical, and commonly geo... |
Title: Energy networks for state estimation with random sensors using sparse labels Abstract: State estimation is required whenever we deal with high-dimensional dynamical systems, as the complete measurement is often unavailable. It is key to gaining insight, performing control or optimizing design tasks. Most deep le... |
Title: Low-Rank Softmax Can Have Unargmaxable Classes in Theory but Rarely in Practice Abstract: Classifiers in natural language processing (NLP) often have a large number of output classes. For example, neural language models (LMs) and machine translation (MT) models both predict tokens from a vocabulary of thousands.... |
Title: A Systematic Review on Computer Vision-Based Parking Lot Management Applied on Public Datasets Abstract: Computer vision-based parking lot management methods have been extensively researched upon owing to their flexibility and cost-effectiveness. To evaluate such methods authors often employ publicly available p... |
Title: Towards On-Device AI and Blockchain for 6G enabled Agricultural Supply-chain Management Abstract: 6G envisions artificial intelligence (AI) powered solutions for enhancing the quality-of-service (QoS) in the network and to ensure optimal utilization of resources. In this work, we propose an architecture based on... |
Title: G$^3$SR: Global Graph Guided Session-based Recommendation Abstract: Session-based recommendation tries to make use of anonymous session data to deliver high-quality recommendation under the condition that user-profiles and the complete historical behavioral data of a target user are unavailable. Previous works c... |
Title: Optimizer Amalgamation Abstract: Selecting an appropriate optimizer for a given problem is of major interest for researchers and practitioners. Many analytical optimizers have been proposed using a variety of theoretical and empirical approaches; however, none can offer a universal advantage over other competiti... |
Title: GATSBI: Generative Adversarial Training for Simulation-Based Inference Abstract: Simulation-based inference (SBI) refers to statistical inference on stochastic models for which we can generate samples, but not compute likelihoods. Like SBI algorithms, generative adversarial networks (GANs) do not require explici... |
Title: TEN: Twin Embedding Networks for the Jigsaw Puzzle Problem with Eroded Boundaries Abstract: The jigsaw puzzle problem (JPP) is a well-known research problem, which has been studied for many years. Solving this problem typically involves a two-stage scheme, consisting of the computation of a pairwise piece compat... |
Title: The worst of both worlds: A comparative analysis of errors in learning from data in psychology and machine learning Abstract: Recent arguments that machine learning (ML) is facing a reproducibility and replication crisis suggest that some published claims in ML research cannot be taken at face value. These conce... |
Title: Wasserstein Adversarial Transformer for Cloud Workload Prediction Abstract: Predictive Virtual Machine (VM) auto-scaling is a promising technique to optimize cloud applications operating costs and performance. Understanding the job arrival rate is crucial for accurately predicting future changes in cloud workloa... |
Title: Sparsity and Heterogeneous Dropout for Continual Learning in the Null Space of Neural Activations Abstract: Continual/lifelong learning from a non-stationary input data stream is a cornerstone of intelligence. Despite their phenomenal performance in a wide variety of applications, deep neural networks are prone ... |
Title: Whats Missing? Learning Hidden Markov Models When the Locations of Missing Observations are Unknown Abstract: The Hidden Markov Model (HMM) is one of the most widely used statistical models for sequential data analysis, and it has been successfully applied in a large variety of domains. One of the key reasons fo... |
Title: Reinforced Imitative Graph Learning for Mobile User Profiling Abstract: Mobile user profiling refers to the efforts of extracting users' characteristics from mobile activities. In order to capture the dynamic varying of user characteristics for generating effective user profiling, we propose an imitation-based m... |
Title: Query-Efficient Black-box Adversarial Attacks Guided by a Transfer-based Prior Abstract: Adversarial attacks have been extensively studied in recent years since they can identify the vulnerability of deep learning models before deployed. In this paper, we consider the black-box adversarial setting, where the adv... |
Title: Symbolic Learning to Optimize: Towards Interpretability and Scalability Abstract: Recent studies on Learning to Optimize (L2O) suggest a promising path to automating and accelerating the optimization procedure for complicated tasks. Existing L2O models parameterize optimization rules by neural networks, and lear... |
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