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Title: From Tensor Network Quantum States to Tensorial Recurrent Neural Networks Abstract: We show that any matrix product state (MPS) can be exactly represented by a recurrent neural network (RNN) with a linear memory update. We generalize this RNN architecture to 2D lattices using a multilinear memory update. It supp... |
Title: Out of distribution robustness with pre-trained Bayesian neural networks Abstract: We develop ShiftMatch, a new training-data-dependent likelihood for out of distribution (OOD) robustness in Bayesian neural networks (BNNs). ShiftMatch is inspired by the training-data-dependent "EmpCov" priors from Izmailov et al... |
Title: Quantifying Inherent Randomness in Machine Learning Algorithms Abstract: Most machine learning (ML) algorithms have several stochastic elements, and their performances are affected by these sources of randomness. This paper uses an empirical study to systematically examine the effects of two sources: randomness ... |
Title: Megapixel Image Generation with Step-Unrolled Denoising Autoencoders Abstract: An ongoing trend in generative modelling research has been to push sample resolutions higher whilst simultaneously reducing computational requirements for training and sampling. We aim to push this trend further via the combination of... |
Title: Segmentation-free PVC for Cardiac SPECT using a Densely-connected Multi-dimensional Dynamic Network Abstract: In nuclear imaging, limited resolution causes partial volume effects (PVEs) that affect image sharpness and quantitative accuracy. Partial volume correction (PVC) methods incorporating high-resolution an... |
Title: HANF: Hyperparameter And Neural Architecture Search in Federated Learning Abstract: Automated machine learning (AutoML) is an important step to make machine learning models being widely applied to solve real world problems. Despite numerous research advancement, machine learning methods are not fully utilized by... |
Title: Achievement and Fragility of Long-term Equitability Abstract: Equipping current decision-making tools with notions of fairness, equitability, or other ethically motivated outcomes, is one of the top priorities in recent research efforts in machine learning, AI, and optimization. In this paper, we investigate how... |
Title: Source Localization of Graph Diffusion via Variational Autoencoders for Graph Inverse Problems Abstract: Graph diffusion problems such as the propagation of rumors, computer viruses, or smart grid failures are ubiquitous and societal. Hence it is usually crucial to identify diffusion sources according to the cur... |
Title: ModLaNets: Learning Generalisable Dynamics via Modularity and Physical Inductive Bias Abstract: Deep learning models are able to approximate one specific dynamical system but struggle at learning generalisable dynamics, where dynamical systems obey the same laws of physics but contain different numbers of elemen... |
Title: How to train accurate BNNs for embedded systems? Abstract: A key enabler of deploying convolutional neural networks on resource-constrained embedded systems is the binary neural network (BNN). BNNs save on memory and simplify computation by binarizing both features and weights. Unfortunately, binarization is ine... |
Title: Learning sparse features can lead to overfitting in neural networks Abstract: It is widely believed that the success of deep networks lies in their ability to learn a meaningful representation of the features of the data. Yet, understanding when and how this feature learning improves performance remains a challe... |
Title: Bugs in Machine Learning-based Systems: A Faultload Benchmark Abstract: The rapid escalation of applying Machine Learning (ML) in various domains has led to paying more attention to the quality of ML components. There is then a growth of techniques and tools aiming at improving the quality of ML components and i... |
Title: Analyzing the impact of SARS-CoV-2 variants on respiratory sound signals Abstract: The COVID-19 outbreak resulted in multiple waves of infections that have been associated with different SARS-CoV-2 variants. Studies have reported differential impact of the variants on respiratory health of patients. We explore w... |
Title: PSP: Million-level Protein Sequence Dataset for Protein Structure Prediction Abstract: Proteins are essential component of human life and their structures are important for function and mechanism analysis. Recent work has shown the potential of AI-driven methods for protein structure prediction. However, the dev... |
Title: Iterative Sound Source Localization for Unknown Number of Sources Abstract: Sound source localization aims to seek the direction of arrival (DOA) of all sound sources from the observed multi-channel audio. For the practical problem of unknown number of sources, existing localization algorithms attempt to predict... |
Title: Physically Consistent Learning of Conservative Lagrangian Systems with Gaussian Processes Abstract: This paper proposes a physically consistent Gaussian Process (GP) enabling the identification of uncertain Lagrangian systems. The function space is tailored according to the energy components of the Lagrangian an... |
Title: Using Autoencoders on Differentially Private Federated Learning GANs Abstract: Machine learning has been applied to almost all fields of computer science over the past decades. The introduction of GANs allowed for new possibilities in fields of medical research and text prediction. However, these new fields work... |
Title: Reinforcement learning based adaptive metaheuristics Abstract: Parameter adaptation, that is the capability to automatically adjust an algorithm's hyperparameters depending on the problem being faced, is one of the main trends in evolutionary computation applied to numerical optimization. While several handcraft... |
Title: Adversarial Robustness of Deep Neural Networks: A Survey from a Formal Verification Perspective Abstract: Neural networks have been widely applied in security applications such as spam and phishing detection, intrusion prevention, and malware detection. This black-box method, however, often has uncertainty and p... |
Title: MPClan: Protocol Suite for Privacy-Conscious Computations Abstract: The growing volumes of data being collected and its analysis to provide better services are creating worries about digital privacy. To address privacy concerns and give practical solutions, the literature has relied on secure multiparty computat... |
Title: Computational Complexity Evaluation of Neural Network Applications in Signal Processing Abstract: In this paper, we provide a systematic approach for assessing and comparing the computational complexity of neural network layers in digital signal processing. We provide and link four software-to-hardware complexit... |
Title: SECLEDS: Sequence Clustering in Evolving Data Streams via Multiple Medoids and Medoid Voting Abstract: Sequence clustering in a streaming environment is challenging because it is computationally expensive, and the sequences may evolve over time. K-medoids or Partitioning Around Medoids (PAM) is commonly used to ... |
Title: Dynamic network congestion pricing based on deep reinforcement learning Abstract: Traffic congestion is a serious problem in urban areas. Dynamic congestion pricing is one of the useful schemes to eliminate traffic congestion in strategic scale. However, in the reality, an optimal dynamic congestion pricing is v... |
Title: "You Can't Fix What You Can't Measure": Privately Measuring Demographic Performance Disparities in Federated Learning Abstract: Federated learning allows many devices to collaborate in the training of machine learning models. As in traditional machine learning, there is a growing concern that models trained with... |
Title: Towards FPGA Implementation of Neural Network-Based Nonlinearity Mitigation Equalizers in Coherent Optical Transmission Systems Abstract: For the first time, recurrent and feedforward neural network-based equalizers for nonlinearity compensation are implemented in an FPGA, with a level of complexity comparable t... |
Title: MULTI-FLGANs: Multi-Distributed Adversarial Networks for Non-IID distribution Abstract: Federated learning is an emerging concept in the domain of distributed machine learning. This concept has enabled GANs to benefit from the rich distributed training data while preserving privacy. However, in a non-iid setting... |
Title: AdAUC: End-to-end Adversarial AUC Optimization Against Long-tail Problems Abstract: It is well-known that deep learning models are vulnerable to adversarial examples. Existing studies of adversarial training have made great progress against this challenge. As a typical trait, they often assume that the class dis... |
Title: Neural Networks with A La Carte Selection of Activation Functions Abstract: Activation functions (AFs), which are pivotal to the success (or failure) of a neural network, have received increased attention in recent years, with researchers seeking to design novel AFs that improve some aspect of network performanc... |
Title: Data-driven discovery of novel 2D materials by deep generative models Abstract: Efficient algorithms to generate candidate crystal structures with good stability properties can play a key role in data-driven materials discovery. Here we show that a crystal diffusion variational autoencoder (CDVAE) is capable of ... |
Title: Multi-Agent Deep Reinforcement Learning for Cost- and Delay-Sensitive Virtual Network Function Placement and Routing Abstract: This paper proposes an effective and novel multiagent deep reinforcement learning (MADRL)-based method for solving the joint virtual network function (VNF) placement and routing (P&R), w... |
Title: Aggregated Multi-output Gaussian Processes with Knowledge Transfer Across Domains Abstract: Aggregate data often appear in various fields such as socio-economics and public security. The aggregate data are associated not with points but with supports (e.g., spatial regions in a city). Since the supports may have... |
Title: SANE-TTS: Stable And Natural End-to-End Multilingual Text-to-Speech Abstract: In this paper, we present SANE-TTS, a stable and natural end-to-end multilingual TTS model. By the difficulty of obtaining multilingual corpus for given speaker, training multilingual TTS model with monolingual corpora is unavoidable. ... |
Title: Implicit Channel Learning for Machine Learning Applications in 6G Wireless Networks Abstract: With the deployment of the fifth generation (5G) wireless systems gathering momentum across the world, possible technologies for 6G are under active research discussions. In particular, the role of machine learning (ML)... |
Title: Self Supervised Learning for Few Shot Hyperspectral Image Classification Abstract: Deep learning has proven to be a very effective approach for Hyperspectral Image (HSI) classification. However, deep neural networks require large annotated datasets to generalize well. This limits the applicability of deep learni... |
Title: Approximating 1-Wasserstein Distance with Trees Abstract: Wasserstein distance, which measures the discrepancy between distributions, shows efficacy in various types of natural language processing (NLP) and computer vision (CV) applications. One of the challenges in estimating Wasserstein distance is that it is ... |
Title: TreeDRNet:A Robust Deep Model for Long Term Time Series Forecasting Abstract: Various deep learning models, especially some latest Transformer-based approaches, have greatly improved the state-of-art performance for long-term time series forecasting.However, those transformer-based models suffer a severe deterio... |
Title: On Structural Explanation of Bias in Graph Neural Networks Abstract: Graph Neural Networks (GNNs) have shown satisfying performance in various graph analytical problems. Hence, they have become the \emph{de facto} solution in a variety of decision-making scenarios. However, GNNs could yield biased results agains... |
Title: zPROBE: Zero Peek Robustness Checks for Federated Learning Abstract: Privacy-preserving federated learning allows multiple users to jointly train a model with coordination of a central server. The server only learns the final aggregation result, thereby preventing leakage of the users' (private) training data fr... |
Title: Classifying Unstructured Clinical Notes via Automatic Weak Supervision Abstract: Healthcare providers usually record detailed notes of the clinical care delivered to each patient for clinical, research, and billing purposes. Due to the unstructured nature of these narratives, providers employ dedicated staff to ... |
Title: Symbolic-Regression Boosting Abstract: Modifying standard gradient boosting by replacing the embedded weak learner in favor of a strong(er) one, we present SyRBo: Symbolic-Regression Boosting. Experiments over 98 regression datasets show that by adding a small number of boosting stages -- between 2--5 -- to a sy... |
Title: Computationally Efficient PAC RL in POMDPs with Latent Determinism and Conditional Embeddings Abstract: We study reinforcement learning with function approximation for large-scale Partially Observable Markov Decision Processes (POMDPs) where the state space and observation space are large or even continuous. Par... |
Title: Multi-modal Sensor Data Fusion for In-situ Classification of Animal Behavior Using Accelerometry and GNSS Data Abstract: We examine using data from multiple sensing modes, i.e., accelerometry and global navigation satellite system (GNSS), for classifying animal behavior. We extract three new features from the GN... |
Title: Synthesizing Rolling Bearing Fault Samples in New Conditions: A framework based on a modified CGAN Abstract: Bearings are one of the vital components of rotating machines that are prone to unexpected faults. Therefore, bearing fault diagnosis and condition monitoring is essential for reducing operational costs a... |
Title: Bilateral Network with Channel Splitting Network and Transformer for Thermal Image Super-Resolution Abstract: In recent years, the Thermal Image Super-Resolution (TISR) problem has become an attractive research topic. TISR would been used in a wide range of fields, including military, medical, agricultural and a... |
Title: How many labelers do you have? A closer look at gold-standard labels Abstract: The construction of most supervised learning datasets revolves around collecting multiple labels for each instance, then aggregating the labels to form a type of ``gold-standard.''. We question the wisdom of this pipeline by developin... |
Title: End-to-End Text-to-Speech Based on Latent Representation of Speaking Styles Using Spontaneous Dialogue Abstract: The recent text-to-speech (TTS) has achieved quality comparable to that of humans; however, its application in spoken dialogue has not been widely studied. This study aims to realize a TTS that closel... |
Title: BYOL-S: Learning Self-supervised Speech Representations by Bootstrapping Abstract: Methods for extracting audio and speech features have been studied since pioneering work on spectrum analysis decades ago. Recent efforts are guided by the ambition to develop general-purpose audio representations. For example, de... |
Title: How to Train Your HiPPO: State Space Models with Generalized Orthogonal Basis Projections Abstract: Linear time-invariant state space models (SSM) are a classical model from engineering and statistics, that have recently been shown to be very promising in machine learning through the Structured State Space seque... |
Title: Phasic Self-Imitative Reduction for Sparse-Reward Goal-Conditioned Reinforcement Learning Abstract: It has been a recent trend to leverage the power of supervised learning (SL) towards more effective reinforcement learning (RL) methods. We propose a novel phasic approach by alternating online RL and offline SL f... |
Title: Provably Efficient Reinforcement Learning in Partially Observable Dynamical Systems Abstract: We study Reinforcement Learning for partially observable dynamical systems using function approximation. We propose a new \textit{Partially Observable Bilinear Actor-Critic framework}, that is general enough to include ... |
Title: Three Applications of Conformal Prediction for Rating Breast Density in Mammography Abstract: Breast cancer is the most common cancers and early detection from mammography screening is crucial in improving patient outcomes. Assessing mammographic breast density is clinically important as the denser breasts have ... |
Title: Knowledge Distillation via Weighted Ensemble of Teaching Assistants Abstract: Knowledge distillation in machine learning is the process of transferring knowledge from a large model called the teacher to a smaller model called the student. Knowledge distillation is one of the techniques to compress the large netw... |
Title: Sampling Enclosing Subgraphs for Link Prediction Abstract: Link prediction is a fundamental problem for graph-structured data (e.g., social networks, drug side-effect networks, etc.). Graph neural networks have offered robust solutions for this problem, specifically by learning the representation of the subgraph... |
Title: STREAMLINE: A Simple, Transparent, End-To-End Automated Machine Learning Pipeline Facilitating Data Analysis and Algorithm Comparison Abstract: Machine learning (ML) offers powerful methods for detecting and modeling associations often in data with large feature spaces and complex associations. Many useful tools... |
Title: The Real Deal: A Review of Challenges and Opportunities in Moving Reinforcement Learning-Based Traffic Signal Control Systems Towards Reality Abstract: Traffic signal control (TSC) is a high-stakes domain that is growing in importance as traffic volume grows globally. An increasing number of works are applying r... |
Title: Efficient and Accurate Top-$K$ Recovery from Choice Data Abstract: The intersection of learning to rank and choice modeling is an active area of research with applications in e-commerce, information retrieval and the social sciences. In some applications such as recommendation systems, the statistician is primar... |
Title: A Disability Lens towards Biases in GPT-3 Generated Open-Ended Languages Abstract: Language models (LM) are becoming prevalent in many language-based application spaces globally. Although these LMs are improving our day-to-day interactions with digital products, concerns remain whether open-ended languages or te... |
Title: Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs Abstract: 3D-related inductive biases like translational invariance and rotational equivariance are indispensable to graph neural networks operating on 3D atomistic graphs such as molecules. Inspired by the success of Transformers in var... |
Title: On making optimal transport robust to all outliers Abstract: Optimal transport (OT) is known to be sensitive against outliers because of its marginal constraints. Outlier robust OT variants have been proposed based on the definition that outliers are samples which are expensive to move. In this paper, we show th... |
Title: Task-Adaptive Few-shot Node Classification Abstract: Node classification is of great importance among various graph mining tasks. In practice, real-world graphs generally follow the long-tail distribution, where a large number of classes only consist of limited labeled nodes. Although Graph Neural Networks (GNNs... |
Title: Learning quantum symmetries with interactive quantum-classical variational algorithms Abstract: A symmetry of a state $\lvert \psi \rangle$ is a unitary operator of which $\lvert \psi \rangle$ is an eigenvector. When $\lvert \psi \rangle$ is an unknown state supplied by a black-box oracle, the state's symmetries... |
Title: Similarity-aware Positive Instance Sampling for Graph Contrastive Pre-training Abstract: Graph instance contrastive learning has been proved as an effective task for Graph Neural Network (GNN) pre-training. However, one key issue may seriously impede the representative power in existing works: Positive instances... |
Title: Affinity-Aware Graph Networks Abstract: Graph Neural Networks (GNNs) have emerged as a powerful technique for learning on relational data. Owing to the relatively limited number of message passing steps they perform -- and hence a smaller receptive field -- there has been significant interest in improving their ... |
Title: World Value Functions: Knowledge Representation for Learning and Planning Abstract: We propose world value functions (WVFs), a type of goal-oriented general value function that represents how to solve not just a given task, but any other goal-reaching task in an agent's environment. This is achieved by equipping... |
Title: Measuring Representational Robustness of Neural Networks Through Shared Invariances Abstract: A major challenge in studying robustness in deep learning is defining the set of ``meaningless'' perturbations to which a given Neural Network (NN) should be invariant. Most work on robustness implicitly uses a human as... |
Title: Set Norm and Equivariant Skip Connections: Putting the Deep in Deep Sets Abstract: Permutation invariant neural networks are a promising tool for making predictions from sets. However, we show that existing permutation invariant architectures, Deep Sets and Set Transformer, can suffer from vanishing or exploding... |
Title: Learning Viewpoint-Agnostic Visual Representations by Recovering Tokens in 3D Space Abstract: Humans are remarkably flexible in understanding viewpoint changes due to visual cortex supporting the perception of 3D structure. In contrast, most of the computer vision models that learn visual representation from a p... |
Title: MaskViT: Masked Visual Pre-Training for Video Prediction Abstract: The ability to predict future visual observations conditioned on past observations and motor commands can enable embodied agents to plan solutions to a variety of tasks in complex environments. This work shows that we can create good video predic... |
Title: On the Parameterization and Initialization of Diagonal State Space Models Abstract: State space models (SSM) have recently been shown to be very effective as a deep learning layer as a promising alternative to sequence models such as RNNs, CNNs, or Transformers. The first version to show this potential was the S... |
Title: Remote Sensing Change Detection (Segmentation) using Denoising Diffusion Probabilistic Models Abstract: Human civilization has an increasingly powerful influence on the earth system, and earth observations are an invaluable tool for assessing and mitigating the negative impacts. To this end, observing precisely ... |
Title: Provably Efficient Model-Free Constrained RL with Linear Function Approximation Abstract: We study the constrained reinforcement learning problem, in which an agent aims to maximize the expected cumulative reward subject to a constraint on the expected total value of a utility function. In contrast to existing m... |
Title: On the Generalizability and Predictability of Recommender Systems Abstract: While other areas of machine learning have seen more and more automation, designing a high-performing recommender system still requires a high level of human effort. Furthermore, recent work has shown that modern recommender system algor... |
Title: Predicting the meal macronutrient composition from continuous glucose monitors Abstract: Sustained high levels of blood glucose in type 2 diabetes (T2DM) can have disastrous long-term health consequences. An essential component of clinical interventions for T2DM is monitoring dietary intake to keep plasma glucos... |
Title: A Topological characterisation of Weisfeiler-Leman equivalence classes Abstract: Graph Neural Networks (GNNs) are learning models aimed at processing graphs and signals on graphs. The most popular and successful GNNs are based on message passing schemes. Such schemes inherently have limited expressive power when... |
Title: Sample Condensation in Online Continual Learning Abstract: Online Continual learning is a challenging learning scenario where the model must learn from a non-stationary stream of data where each sample is seen only once. The main challenge is to incrementally learn while avoiding catastrophic forgetting, namely ... |
Title: Quant-BnB: A Scalable Branch-and-Bound Method for Optimal Decision Trees with Continuous Features Abstract: Decision trees are one of the most useful and popular methods in the machine learning toolbox. In this paper, we consider the problem of learning optimal decision trees, a combinatorial optimization proble... |
Title: Non-Determinism and the Lawlessness of ML Code Abstract: Legal literature on machine learning (ML) tends to focus on harms, and as a result tends to reason about individual model outcomes and summary error rates. This focus on model-level outcomes and errors has masked important aspects of ML that are rooted in ... |
Title: CoSP: Co-supervised pretraining of pocket and ligand Abstract: Can we inject the pocket-ligand interaction knowledge into the pre-trained model and jointly learn their chemical space? Pretraining molecules and proteins has attracted considerable attention in recent years, while most of these approaches focus on ... |
Title: Inductive Conformal Prediction: A Straightforward Introduction with Examples in Python Abstract: Inductive Conformal Prediction (ICP) is a set of distribution-free and model agnostic algorithms devised to predict with a user-defined confidence with coverage guarantee. Instead of having point predictions, i.e., a... |
Title: Video PreTraining (VPT): Learning to Act by Watching Unlabeled Online Videos Abstract: Pretraining on noisy, internet-scale datasets has been heavily studied as a technique for training models with broad, general capabilities for text, images, and other modalities. However, for many sequential decision domains s... |
Title: Authentication of Copy Detection Patterns under Machine Learning Attacks: A Supervised Approach Abstract: Copy detection patterns (CDP) are an attractive technology that allows manufacturers to defend their products against counterfeiting. The main assumption behind the protection mechanism of CDP is that these ... |
Title: Open-source FPGA-ML codesign for the MLPerf Tiny Benchmark Abstract: We present our development experience and recent results for the MLPerf Tiny Inference Benchmark on field-programmable gate array (FPGA) platforms. We use the open-source hls4ml and FINN workflows, which aim to democratize AI-hardware codesign ... |
Title: Chasing Convex Bodies and Functions with Black-Box Advice Abstract: We consider the problem of convex function chasing with black-box advice, where an online decision-maker aims to minimize the total cost of making and switching between decisions in a normed vector space, aided by black-box advice such as the de... |
Title: Graph Neural Networks for Temperature-Dependent Activity Coefficient Prediction of Solutes in Ionic Liquids Abstract: Ionic liquids (ILs) are important solvents for sustainable processes and predicting activity coefficients (ACs) of solutes in ILs is needed. Recently, matrix completion methods (MCMs), transforme... |
Title: Single-phase deep learning in cortico-cortical networks Abstract: The error-backpropagation (backprop) algorithm remains the most common solution to the credit assignment problem in artificial neural networks. In neuroscience, it is unclear whether the brain could adopt a similar strategy to correctly modify its... |
Title: Measuring the Feasibility of Analogical Transfer using Complexity Abstract: Analogies are 4-ary relations of the form "A is to B as C is to D". While focus has been mostly on how to solve an analogy, i.e. how to find correct values of D given A, B and C, less attention has been drawn on whether solving such an a... |
Title: Classical surrogates for quantum learning models Abstract: The advent of noisy intermediate-scale quantum computers has put the search for possible applications to the forefront of quantum information science. One area where hopes for an advantage through near-term quantum computers are high is quantum machine l... |
Title: NovelCraft: A Dataset for Novelty Detection and Discovery in Open Worlds Abstract: In order for artificial agents to perform useful tasks in changing environments, they must be able to both detect and adapt to novelty. However, visual novelty detection research often only evaluates on repurposed datasets such as... |
Title: Walk the Random Walk: Learning to Discover and Reach Goals Without Supervision Abstract: Learning a diverse set of skills by interacting with an environment without any external supervision is an important challenge. In particular, obtaining a goal-conditioned agent that can reach any given state is useful in ma... |
Title: Self-Supervised Training with Autoencoders for Visual Anomaly Detection Abstract: Deep convolutional autoencoders provide an effective tool for learning non-linear dimensionality reduction in an unsupervised way. Recently, they have been used for the task of anomaly detection in the visual domain. By optimising ... |
Title: Measurement and applications of position bias in a marketplace search engine Abstract: Search engines intentionally influence user behavior by picking and ranking the list of results. Users engage with the highest results both because of their prominent placement and because they are typically the most relevant ... |
Title: AST-Probe: Recovering abstract syntax trees from hidden representations of pre-trained language models Abstract: The objective of pre-trained language models is to learn contextual representations of textual data. Pre-trained language models have become mainstream in natural language processing and code modeling... |
Title: Deep Reinforcement Learning-Assisted Federated Learning for Robust Short-term Utility Demand Forecasting in Electricity Wholesale Markets Abstract: Short-term load forecasting (STLF) plays a significant role in the operation of electricity trading markets. Considering the growing concern of data privacy, federat... |
Title: Reinforcement Learning under Partial Observability Guided by Learned Environment Models Abstract: In practical applications, we can rarely assume full observability of a system's environment, despite such knowledge being important for determining a reactive control system's precise interaction with its environme... |
Title: A Temporal Extension of Latent Dirichlet Allocation for Unsupervised Acoustic Unit Discovery Abstract: Latent Dirichlet allocation (LDA) is widely used for unsupervised topic modelling on sets of documents. No temporal information is used in the model. However, there is often a relationship between the correspon... |
Title: Efficient Transformer-based Speech Enhancement Using Long Frames and STFT Magnitudes Abstract: The SepFormer architecture shows very good results in speech separation. Like other learned-encoder models, it uses short frames, as they have been shown to obtain better performance in these cases. This results in a l... |
Title: Learning Agile Skills via Adversarial Imitation of Rough Partial Demonstrations Abstract: Learning agile skills is one of the main challenges in robotics. To this end, reinforcement learning approaches have achieved impressive results. These methods require explicit task information in terms of a reward function... |
Title: A generalised form for a homogeneous population of structures using an overlapping mixture of Gaussian processes Abstract: Reductions in natural frequency are often used as a damage indicator for structural health monitoring (SHM) purposes. However, fluctuations in operational and environmental conditions, chang... |
Title: EFFGAN: Ensembles of fine-tuned federated GANs Abstract: Generative adversarial networks have proven to be a powerful tool for learning complex and high-dimensional data distributions, but issues such as mode collapse have been shown to make it difficult to train them. This is an even harder problem when the dat... |
Title: Capacity Optimality of OAMP in Coded Large Unitarily Invariant Systems Abstract: This paper investigates a large unitarily invariant system (LUIS) involving a unitarily invariant sensing matrix, an arbitrary fixed signal distribution, and forward error control (FEC) coding. Several area properties are establishe... |
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