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Title: MuZero with Self-competition for Rate Control in VP9 Video Compression Abstract: Video streaming usage has seen a significant rise as entertainment, education, and business increasingly rely on online video. Optimizing video compression has the potential to increase access and quality of content to users, and re... |
Title: Continuous-time stochastic gradient descent for optimizing over the stationary distribution of stochastic differential equations Abstract: We develop a new continuous-time stochastic gradient descent method for optimizing over the stationary distribution of stochastic differential equation (SDE) models. The algo... |
Title: On the Complexity of Object Detection on Real-world Public Transportation Images for Social Distancing Measurement Abstract: Social distancing in public spaces has become an essential aspect in helping to reduce the impact of the COVID-19 pandemic. Exploiting recent advances in machine learning, there have been ... |
Title: Out of Thin Air: Is Zero-Shot Cross-Lingual Keyword Detection Better Than Unsupervised? Abstract: Keyword extraction is the task of retrieving words that are essential to the content of a given document. Researchers proposed various approaches to tackle this problem. At the top-most level, approaches are divided... |
Title: The Impact of Batch Learning in Stochastic Linear Bandits Abstract: We consider a special case of bandit problems, named batched bandits, in which an agent observes batches of responses over a certain time period. Unlike previous work, we consider a practically relevant batch-centric scenario of batch learning. ... |
Title: PFGE: Parsimonious Fast Geometric Ensembling of DNNs Abstract: Ensemble methods have been widely used to improve the performance of machine learning methods in terms of generalization, while they are hard to use in deep learning systems, as training an ensemble of deep neural networks (DNNs) incurs an extremely ... |
Title: Learning Weakly-Supervised Contrastive Representations Abstract: We argue that a form of the valuable information provided by the auxiliary information is its implied data clustering information. For instance, considering hashtags as auxiliary information, we can hypothesize that an Instagram image will be seman... |
Title: Partially Fake Audio Detection by Self-attention-based Fake Span Discovery Abstract: The past few years have witnessed the significant advances of speech synthesis and voice conversion technologies. However, such technologies can undermine the robustness of broadly implemented biometric identification models and... |
Title: CodeFill: Multi-token Code Completion by Jointly Learning from Structure and Naming Sequences Abstract: Code completion is an essential feature of IDEs, yet current autocompleters are restricted to either grammar-based or NLP-based single token completions. Both approaches have significant drawbacks: grammar-bas... |
Title: Versatile Dueling Bandits: Best-of-both-World Analyses for Online Learning from Preferences Abstract: We study the problem of $K$-armed dueling bandit for both stochastic and adversarial environments, where the goal of the learner is to aggregate information through relative preferences of pair of decisions poin... |
Title: Spiking Cochlea with System-level Local Automatic Gain Control Abstract: Including local automatic gain control (AGC) circuitry into a silicon cochlea design has been challenging because of transistor mismatch and model complexity. To address this, we present an alternative system-level algorithm that implements... |
Title: How Do Vision Transformers Work? Abstract: The success of multi-head self-attentions (MSAs) for computer vision is now indisputable. However, little is known about how MSAs work. We present fundamental explanations to help better understand the nature of MSAs. In particular, we demonstrate the following properti... |
Title: A Data-Centric Approach to Generate Invariants for a Smart Grid Using Machine Learning Abstract: Cyber-Physical Systems (CPS) have gained popularity due to the increased requirements on their uninterrupted connectivity and process automation. Due to their connectivity over the network including intranet and inte... |
Title: A Graph-based U-Net Model for Predicting Traffic in unseen Cities Abstract: Accurate traffic prediction is a key ingredient to enable traffic management like rerouting cars to reduce road congestion or regulating traffic via dynamic speed limits to maintain a steady flow. A way to represent traffic data is in th... |
Title: Graph-GAN: A spatial-temporal neural network for short-term passenger flow prediction in urban rail transit systems Abstract: Short-term passenger flow prediction plays an important role in better managing the urban rail transit (URT) systems. Emerging deep learning models provide good insights to improve short-... |
Title: Learning Branch Probabilities in Compiler from Datacenter Workloads Abstract: Estimating the probability with which a conditional branch instruction is taken is an important analysis that enables many optimizations in modern compilers. When using Profile Guided Optimizations (PGO), compilers are able to make a g... |
Title: Minimizing Entropy to Discover Good Solutions to Recurrent Mixed Integer Programs Abstract: Current state-of-the-art solvers for mixed-integer programming (MIP) problems are designed to perform well on a wide range of problems. However, for many real-world use cases, problem instances come from a narrow distribu... |
Title: Attention-based Deep Neural Networks for Battery Discharge Capacity Forecasting Abstract: Battery discharge capacity forecasting is critically essential for the applications of lithium-ion batteries. The capacity degeneration can be treated as the memory of the initial battery state of charge from the data point... |
Title: Trace norm regularization for multi-task learning with scarce data Abstract: Multi-task learning leverages structural similarities between multiple tasks to learn despite very few samples. Motivated by the recent success of neural networks applied to data-scarce tasks, we consider a linear low-dimensional shared... |
Title: Information Flow in Deep Neural Networks Abstract: Although deep neural networks have been immensely successful, there is no comprehensive theoretical understanding of how they work or are structured. As a result, deep networks are often seen as black boxes with unclear interpretations and reliability. Understan... |
Title: Speech Analysis for Automatic Mania Assessment in Bipolar Disorder Abstract: Bipolar disorder is a mental disorder that causes periods of manic and depressive episodes. In this work, we classify recordings from Bipolar Disorder corpus that contain 7 different tasks, into hypomania, mania, and remission classes u... |
Title: Wukong: 100 Million Large-scale Chinese Cross-modal Pre-training Dataset and A Foundation Framework Abstract: Vision-Language Pre-training (VLP) models have shown remarkable performance on various downstream tasks. Their success heavily relies on the scale of pre-trained cross-modal datasets. However, the lack o... |
Title: Punctuation restoration in Swedish through fine-tuned KB-BERT Abstract: Presented here is a method for automatic punctuation restoration in Swedish using a BERT model. The method is based on KB-BERT, a publicly available, neural network language model pre-trained on a Swedish corpus by National Library of Sweden... |
Title: Aspect Based Sentiment Analysis Using Spectral Temporal Graph Neural Network Abstract: The objective of Aspect Based Sentiment Analysis is to capture the sentiment of reviewers associated with different aspects. However, complexity of the review sentences, presence of double negation and specific usage of words ... |
Title: Design of Explainability Module with Experts in the Loop for Visualization and Dynamic Adjustment of Continual Learning Abstract: Continual learning can enable neural networks to evolve by learning new tasks sequentially in task-changing scenarios. However, two general and related challenges should be overcome i... |
Title: Flexible learning of quantum states with generative query neural networks Abstract: Deep neural networks are a powerful tool for characterizing quantum states. In this task, neural networks are typically trained with measurement data gathered from the quantum state to be characterized. But is it possible to trai... |
Title: Learning from distinctive candidates to optimize reduced-precision convolution program on tensor cores Abstract: Convolution is one of the fundamental operations of deep neural networks with demanding matrix computation. In a graphic processing unit (GPU), Tensor Core is a specialized matrix processing hardware ... |
Title: Development and Comparison of Scoring Functions in Curriculum Learning Abstract: Curriculum Learning is the presentation of samples to the machine learning model in a meaningful order instead of a random order. The main challenge of Curriculum Learning is determining how to rank these samples. The ranking of the... |
Title: On the Chattering of SARSA with Linear Function Approximation Abstract: SARSA, a classical on-policy control algorithm for reinforcement learning, is known to chatter when combined with linear function approximation: SARSA does not diverge but oscillates in a bounded region. However, little is know about how fas... |
Title: Memory Efficient Tries for Sequential Pattern Mining Abstract: The rapid and continuous growth of data has increased the need for scalable mining algorithms in unsupervised learning and knowledge discovery. In this paper, we focus on Sequential Pattern Mining (SPM), a fundamental topic in knowledge discovery tha... |
Title: A Machine Learning Framework for Event Identification via Modal Analysis of PMU Data Abstract: Power systems are prone to a variety of events (e.g. line trips and generation loss) and real-time identification of such events is crucial in terms of situational awareness, reliability, and security. Using measuremen... |
Title: Continual Learning from Demonstration of Robotic Skills Abstract: Methods for teaching motion skills to robots focus on training for a single skill at a time. Robots capable of learning from demonstration can considerably benefit from the added ability to learn new movements without forgetting past knowledge. To... |
Title: On Pitfalls of Identifiability in Unsupervised Learning. A Note on: "Desiderata for Representation Learning: A Causal Perspective" Abstract: Model identifiability is a desirable property in the context of unsupervised representation learning. In absence thereof, different models may be observationally indistingu... |
Title: HAKE: A Knowledge Engine Foundation for Human Activity Understanding Abstract: Human activity understanding is of widespread interest in artificial intelligence and spans diverse applications like health care and behavior analysis. Although there have been advances with deep learning, it remains challenging. The... |
Title: HyLa: Hyperbolic Laplacian Features For Graph Learning Abstract: Due to its geometric properties, hyperbolic space can support high-fidelity embeddings of tree- and graph-structured data. For graph learning, points in hyperbolic space have been used successfully as signals in deep neural networks: e.g. hyperboli... |
Title: Domain-Adjusted Regression or: ERM May Already Learn Features Sufficient for Out-of-Distribution Generalization Abstract: A common explanation for the failure of deep networks to generalize out-of-distribution is that they fail to recover the "correct" features. Focusing on the domain generalization setting, we ... |
Title: Physics-Informed Deep Monte Carlo Quantile Regression method for Interval Multilevel Bayesian Network-based Satellite Heat Reliability Analysis Abstract: Temperature field reconstruction is essential for analyzing satellite heat reliability. As a representative machine learning model, the deep convolutional neur... |
Title: Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations Abstract: The evaluation of explanation methods is a research topic that has not yet been explored deeply, however, since explainability is supposed to strengthen trust in artificial intelligence, it is necessary to syst... |
Title: Delaunay Component Analysis for Evaluation of Data Representations Abstract: Advanced representation learning techniques require reliable and general evaluation methods. Recently, several algorithms based on the common idea of geometric and topological analysis of a manifold approximated from the learned data re... |
Title: A Graphical Approach For Brain Haemorrhage Segmentation Abstract: Haemorrhaging of the brain is the leading cause of death in people between the ages of 15 and 24 and the third leading cause of death in people older than that. Computed tomography (CT) is an imaging modality used to diagnose neurological emergenc... |
Title: Black-Box Generalization Abstract: We provide the first generalization error analysis for black-box learning through derivative-free optimization. Under the assumption of a Lipschitz and smooth unknown loss, we consider the Zeroth-order Stochastic Search (ZoSS) algorithm, that updates a $d$-dimensional model by ... |
Title: Active Surrogate Estimators: An Active Learning Approach to Label-Efficient Model Evaluation Abstract: We propose Active Surrogate Estimators (ASEs), a new method for label-efficient model evaluation. Evaluating model performance is a challenging and important problem when labels are expensive. ASEs address this... |
Title: Counterfactual inference for sequential experimental design Abstract: We consider the problem of counterfactual inference in sequentially designed experiments wherein a collection of $\mathbf{N}$ units each undergo a sequence of interventions for $\mathbf{T}$ time periods, based on policies that sequentially ada... |
Title: DeCorus: Hierarchical Multivariate Anomaly Detection at Cloud-Scale Abstract: Multivariate anomaly detection can be used to identify outages within large volumes of telemetry data for computing systems. However, developing an efficient anomaly detector that can provide users with relevant information is a challe... |
Title: FOLD-RM: A Scalable, Efficient, and Explainable Inductive Learning Algorithm for Multi-Category Classification of Mixed Data Abstract: FOLD-RM is an automated inductive learning algorithm for learning default rules for mixed (numerical and categorical) data. It generates an (explainable) answer set programming (... |
Title: A Generic Self-Supervised Framework of Learning Invariant Discriminative Features Abstract: Self-supervised learning (SSL) has become a popular method for generating invariant representations without the need for human annotations. Nonetheless, the desired invariant representation is achieved by utilising prior ... |
Title: Stochastic linear optimization never overfits with quadratically-bounded losses on general data Abstract: This work shows that a diverse collection of linear optimization methods, when run on general data, fail to overfit, despite lacking any explicit constraints or regularization: with high probability, their t... |
Title: Convex Programs and Lyapunov Functions for Reinforcement Learning: A Unified Perspective on the Analysis of Value-Based Methods Abstract: Value-based methods play a fundamental role in Markov decision processes (MDPs) and reinforcement learning (RL). In this paper, we present a unified control-theoretic framewor... |
Title: Do Gradient Inversion Attacks Make Federated Learning Unsafe? Abstract: Federated learning (FL) allows the collaborative training of AI models without needing to share raw data. This capability makes it especially interesting for healthcare applications where patient and data privacy is of utmost concern. Howeve... |
Title: Tensor Moments of Gaussian Mixture Models: Theory and Applications Abstract: Gaussian mixture models (GMMs) are fundamental tools in statistical and data sciences. We study the moments of multivariate Gaussians and GMMs. The $d$-th moment of an $n$-dimensional random variable is a symmetric $d$-way tensor of siz... |
Title: Slicing Aided Hyper Inference and Fine-tuning for Small Object Detection Abstract: Detection of small objects and objects far away in the scene is a major challenge in surveillance applications. Such objects are represented by small number of pixels in the image and lack sufficient details, making them difficult... |
Title: Repairing the Cracked Foundation: A Survey of Obstacles in Evaluation Practices for Generated Text Abstract: Evaluation practices in natural language generation (NLG) have many known flaws, but improved evaluation approaches are rarely widely adopted. This issue has become more urgent, since neural NLG models ha... |
Title: Semi-Equivariant GNN Architectures for Jet Tagging Abstract: Composing Graph Neural Networks (GNNs) of operations that respect physical symmetries has been suggested to give better model performance with a smaller number of learnable parameters. However, real-world applications, such as in high energy physics ha... |
Title: Towards Best Practice of Interpreting Deep Learning Models for EEG-based Brain Computer Interfaces Abstract: Understanding deep learning models is important for EEG-based brain-computer interface (BCI), since it not only can boost trust of end users but also potentially shed light on reasons that cause a model t... |
Title: Minimax in Geodesic Metric Spaces: Sion's Theorem and Algorithms Abstract: Determining whether saddle points exist or are approximable for nonconvex-nonconcave problems is usually intractable. We take a step towards understanding certain nonconvex-nonconcave minimax problems that do remain tractable. Specificall... |
Title: DermX: an end-to-end framework for explainable automated dermatological diagnosis Abstract: Dermatological diagnosis automation is essential in addressing the high prevalence of skin diseases and critical shortage of dermatologists. Despite approaching expert-level diagnosis performance, convolutional neural net... |
Title: Deep Ensembles Work, But Are They Necessary? Abstract: Ensembling neural networks is an effective way to increase accuracy, and can often match the performance of larger models. This observation poses a natural question: given the choice between a deep ensemble and a single neural network with similar accuracy, ... |
Title: Learned Turbulence Modelling with Differentiable Fluid Solvers Abstract: In this paper, we train turbulence models based on convolutional neural networks. These learned turbulence models improve under-resolved low resolution solutions to the incompressible Navier-Stokes equations at simulation time. Our method i... |
Title: Transformer Memory as a Differentiable Search Index Abstract: In this paper, we demonstrate that information retrieval can be accomplished with a single Transformer, in which all information about the corpus is encoded in the parameters of the model. To this end, we introduce the Differentiable Search Index (DSI... |
Title: Unlabeled Data Help: Minimax Analysis and Adversarial Robustness Abstract: The recent proposed self-supervised learning (SSL) approaches successfully demonstrate the great potential of supplementing learning algorithms with additional unlabeled data. However, it is still unclear whether the existing SSL algorith... |
Title: Continuously Generalized Ordinal Regression for Linear and Deep Models Abstract: Ordinal regression is a classification task where classes have an order and prediction error increases the further the predicted class is from the true class. The standard approach for modeling ordinal data involves fitting parallel... |
Title: Robust Policy Learning over Multiple Uncertainty Sets Abstract: Reinforcement learning (RL) agents need to be robust to variations in safety-critical environments. While system identification methods provide a way to infer the variation from online experience, they can fail in settings where fast identification ... |
Title: Strategy Discovery and Mixture in Lifelong Learning from Heterogeneous Demonstration Abstract: Learning from Demonstration (LfD) approaches empower end-users to teach robots novel tasks via demonstrations of the desired behaviors, democratizing access to robotics. A key challenge in LfD research is that users te... |
Title: QuadSim: A Quadcopter Rotational Dynamics Simulation Framework For Reinforcement Learning Algorithms Abstract: This study focuses on designing and developing a mathematically based quadcopter rotational dynamics simulation framework for testing reinforcement learning (RL) algorithms in many flexible configuratio... |
Title: Recurrent Neural Networks for Dynamical Systems: Applications to Ordinary Differential Equations, Collective Motion, and Hydrological Modeling Abstract: Classical methods of solving spatiotemporal dynamical systems include statistical approaches such as autoregressive integrated moving average, which assume line... |
Title: Analysis of Neural Fragility: Bounding the Norm of a Rank-One Perturbation Matrix Abstract: Over 15 million epilepsy patients worldwide do not respond to drugs and require surgical treatment. Successful surgical treatment requires complete removal, or disconnection of the epileptogenic zone (EZ), but without a p... |
Title: One Step at a Time: Long-Horizon Vision-and-Language Navigation with Milestones Abstract: We study the problem of developing autonomous agents that can follow human instructions to infer and perform a sequence of actions to complete the underlying task. Significant progress has been made in recent years, especia... |
Title: Benchmarking Online Sequence-to-Sequence and Character-based Handwriting Recognition from IMU-Enhanced Pens Abstract: Handwriting is one of the most frequently occurring patterns in everyday life and with it come challenging applications such as handwriting recognition (HWR), writer identification, and signature... |
Title: Principal Manifold Flows Abstract: Normalizing flows map an independent set of latent variables to their samples using a bijective transformation. Despite the exact correspondence between samples and latent variables, their high level relationship is not well understood. In this paper we characterize the geometr... |
Title: Orthogonalising gradients to speed up neural network optimisation Abstract: The optimisation of neural networks can be sped up by orthogonalising the gradients before the optimisation step, ensuring the diversification of the learned representations. We orthogonalise the gradients of the layer's components/filte... |
Title: A Unified Perspective on Value Backup and Exploration in Monte-Carlo Tree Search Abstract: Monte-Carlo Tree Search (MCTS) is a class of methods for solving complex decision-making problems through the synergy of Monte-Carlo planning and Reinforcement Learning (RL). The highly combinatorial nature of the problems... |
Title: Discriminability-enforcing loss to improve representation learning Abstract: During the training process, deep neural networks implicitly learn to represent the input data samples through a hierarchy of features, where the size of the hierarchy is determined by the number of layers. In this paper, we focus on en... |
Title: Synthetically Controlled Bandits Abstract: This paper presents a new dynamic approach to experiment design in settings where, due to interference or other concerns, experimental units are coarse. `Region-split' experiments on online platforms are one example of such a setting. The cost, or regret, of experimenta... |
Title: Graph Neural Networks for Graphs with Heterophily: A Survey Abstract: Recent years have witnessed fast developments of graph neural networks (GNNs) that have benefited myriads of graph analytic tasks and applications. In general, most GNNs depend on the homophily assumption that nodes belonging to the same class... |
Title: Scaling up Ranking under Constraints for Live Recommendations by Replacing Optimization with Prediction Abstract: Many important multiple-objective decision problems can be cast within the framework of ranking under constraints and solved via a weighted bipartite matching linear program. Some of these optimizati... |
Title: Statistical Inference After Adaptive Sampling in Non-Markovian Environments Abstract: There is a great desire to use adaptive sampling methods, such as reinforcement learning (RL) and bandit algorithms, for the real-time personalization of interventions in digital applications like mobile health and education. A... |
Title: A Survey on Dynamic Neural Networks for Natural Language Processing Abstract: Effectively scaling large Transformer models is a main driver of recent advances in natural language processing. Dynamic neural networks, as an emerging research direction, are capable of scaling up neural networks with sub-linear incr... |
Title: A Survey on Model Compression for Natural Language Processing Abstract: With recent developments in new architectures like Transformer and pretraining techniques, significant progress has been made in applications of natural language processing (NLP). However, the high energy cost and long inference delay of Tra... |
Title: Recent Advances in Reliable Deep Graph Learning: Adversarial Attack, Inherent Noise, and Distribution Shift Abstract: Deep graph learning (DGL) has achieved remarkable progress in both business and scientific areas ranging from finance and e-commerce to drug and advanced material discovery. Despite the progress,... |
Title: Transformers in Time Series: A Survey Abstract: Transformers have achieved superior performances in many tasks in natural language processing and computer vision, which also intrigues great interests in the time series community. Among multiple advantages of transformers, the ability to capture long-range depend... |
Title: STaR: Knowledge Graph Embedding by Scaling, Translation and Rotation Abstract: The bilinear method is mainstream in Knowledge Graph Embedding (KGE), aiming to learn low-dimensional representations for entities and relations in Knowledge Graph (KG) and complete missing links. Most of the existing works are to fin... |
Title: Memory via Temporal Delays in weightless Spiking Neural Network Abstract: A common view in the neuroscience community is that memory is encoded in the connection strength between neurons. This perception led artificial neural network models to focus on connection weights as the key variables to modulate learning... |
Title: Compositional Scene Representation Learning via Reconstruction: A Survey Abstract: Visual scene representation learning is an important research problem in the field of computer vision. The performance of artificial intelligence systems on vision tasks could be improved if more suitable representations are learn... |
Title: Debiased Self-Training for Semi-Supervised Learning Abstract: Deep neural networks achieve remarkable performances on a wide range of tasks with the aid of large-scale labeled datasets. Yet these datasets are time-consuming and labor-exhaustive to obtain on realistic tasks. To mitigate the requirement for labele... |
Title: Machine Learning in Aerodynamic Shape Optimization Abstract: Large volumes of experimental and simulation aerodynamic data have been rapidly advancing aerodynamic shape optimization (ASO) via machine learning (ML), whose effectiveness has been growing thanks to continued developments in deep learning. In this re... |
Title: L2C2: Locally Lipschitz Continuous Constraint towards Stable and Smooth Reinforcement Learning Abstract: This paper proposes a new regularization technique for reinforcement learning (RL) towards making policy and value functions smooth and stable. RL is known for the instability of the learning process and the ... |
Title: OLIVE: Oblivious and Differentially Private Federated Learning on Trusted Execution Environment Abstract: Differentially private federated learning (DP-FL) has received increasing attention to mitigate the privacy risk in federated learning. Although different schemes for DP-FL have been proposed, there is still... |
Title: Fairness Amidst Non-IID Graph Data: A Literature Review Abstract: Fairness in machine learning (ML), the process to understand and correct algorithmic bias, has gained increasing attention with numerous literature being carried out, commonly assume the underlying data is independent and identically distributed (... |
Title: TURF: A Two-factor, Universal, Robust, Fast Distribution Learning Algorithm Abstract: Approximating distributions from their samples is a canonical statistical-learning problem. One of its most powerful and successful modalities approximates every distribution to an $\ell_1$ distance essentially at most a consta... |
Title: DeepONet-Grid-UQ: A Trustworthy Deep Operator Framework for Predicting the Power Grid's Post-Fault Trajectories Abstract: This paper proposes a new data-driven method for the reliable prediction of power system post-fault trajectories. The proposed method is based on the fundamentally new concept of Deep Operato... |
Title: Federated Learning with Sparsified Model Perturbation: Improving Accuracy under Client-Level Differential Privacy Abstract: Federated learning (FL) that enables distributed clients to collaboratively learn a shared statistical model while keeping their training data locally has received great attention recently ... |
Title: G-Mixup: Graph Data Augmentation for Graph Classification Abstract: This work develops \emph{mixup for graph data}. Mixup has shown superiority in improving the generalization and robustness of neural networks by interpolating features and labels between two random samples. Traditionally, Mixup can work on regul... |
Title: On the Origins of the Block Structure Phenomenon in Neural Network Representations Abstract: Recent work has uncovered a striking phenomenon in large-capacity neural networks: they contain blocks of contiguous hidden layers with highly similar representations. This block structure has two seemingly contradictory... |
Title: Improving Human Sperm Head Morphology Classification with Unsupervised Anatomical Feature Distillation Abstract: With rising male infertility, sperm head morphology classification becomes critical for accurate and timely clinical diagnosis. Recent deep learning (DL) morphology analysis methods achieve promising ... |
Title: One-bit Submission for Locally Private Quasi-MLE: Its Asymptotic Normality and Limitation Abstract: Local differential privacy~(LDP) is an information-theoretic privacy definition suitable for statistical surveys that involve an untrusted data curator. An LDP version of quasi-maximum likelihood estimator~(QMLE) ... |
Title: Unsupervised word-level prosody tagging for controllable speech synthesis Abstract: Although word-level prosody modeling in neural text-to-speech (TTS) has been investigated in recent research for diverse speech synthesis, it is still challenging to control speech synthesis manually without a specific reference.... |
Title: Holistic Adversarial Robustness of Deep Learning Models Abstract: Adversarial robustness studies the worst-case performance of a machine learning model to ensure safety and reliability. With the proliferation of deep-learning based technology, the potential risks associated with model development and deployment ... |
Title: Multi-style Training for South African Call Centre Audio Abstract: Mismatched data is a challenging problem for automatic speech recognition (ASR) systems. One of the most common techniques used to address mismatched data is multi-style training (MTR), a form of data augmentation that attempts to transform the t... |
Title: Navigating Local Minima in Quantized Spiking Neural Networks Abstract: Spiking and Quantized Neural Networks (NNs) are becoming exceedingly important for hyper-efficient implementations of Deep Learning (DL) algorithms. However, these networks face challenges when trained using error backpropagation, due to the ... |
Title: Geometrically Equivariant Graph Neural Networks: A Survey Abstract: Many scientific problems require to process data in the form of geometric graphs. Unlike generic graph data, geometric graphs exhibit symmetries of translations, rotations, and/or reflections. Researchers have leveraged such inductive bias and d... |
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