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Title: Is Fairness Only Metric Deep? Evaluating and Addressing Subgroup Gaps in Deep Metric Learning Abstract: Deep metric learning (DML) enables learning with less supervision through its emphasis on the similarity structure of representations. There has been much work on improving generalization of DML in settings li... |
Title: Mokey: Enabling Narrow Fixed-Point Inference for Out-of-the-Box Floating-Point Transformer Models Abstract: Increasingly larger and better Transformer models keep advancing state-of-the-art accuracy and capability for Natural Language Processing applications. These models demand more computational power, storage... |
Title: Efficient Exploration via First-Person Behavior Cloning Assisted Rapidly-Exploring Random Trees Abstract: Modern day computer games have extremely large state and action spaces. To detect bugs in these games' models, human testers play the games repeatedly to explore the game and find errors in the games. Such g... |
Title: Bellman Residual Orthogonalization for Offline Reinforcement Learning Abstract: We introduce a new reinforcement learning principle that approximates the Bellman equations by enforcing their validity only along an user-defined space of test functions. Focusing on applications to model-free offline RL with functi... |
Title: DPar2: Fast and Scalable PARAFAC2 Decomposition for Irregular Dense Tensors Abstract: Given an irregular dense tensor, how can we efficiently analyze it? An irregular tensor is a collection of matrices whose columns have the same size and rows have different sizes from each other. PARAFAC2 decomposition is a fun... |
Title: A Two-Stage Federated Transfer Learning Framework in Medical Images Classification on Limited Data: A COVID-19 Case Study Abstract: COVID-19 pandemic has spread rapidly and caused a shortage of global medical resources. The efficiency of COVID-19 diagnosis has become highly significant. As deep learning and conv... |
Title: On Understanding and Mitigating the Dimensional Collapse of Graph Contrastive Learning: a Non-Maximum Removal Approach Abstract: Graph Contrastive Learning (GCL) has shown promising performance in graph representation learning (GRL) without the supervision of manual annotations. GCL can generate graph-level embe... |
Title: LHNN: Lattice Hypergraph Neural Network for VLSI Congestion Prediction Abstract: Precise congestion prediction from a placement solution plays a crucial role in circuit placement. This work proposes the lattice hypergraph (LH-graph), a novel graph formulation for circuits, which preserves netlist data during the... |
Title: Risk Consistent Multi-Class Learning from Label Proportions Abstract: This study addresses a multiclass learning from label proportions (MCLLP) setting in which training instances are provided in bags and only the proportion of each class within the bags is provided. Most existing MCLLP methods impose bag-wise c... |
Title: Graph Neural Networks in Particle Physics: Implementations, Innovations, and Challenges Abstract: Many physical systems can be best understood as sets of discrete data with associated relationships. Where previously these sets of data have been formulated as series or image data to match the available machine le... |
Title: Direct evaluation of progression or regression of disease burden in brain metastatic disease with Deep Neuroevolution Abstract: Purpose: A core component of advancing cancer treatment research is assessing response to therapy. Doing so by hand, for example as per RECIST or RANO criteria, is tedious, time-consumi... |
Title: Beyond Fixation: Dynamic Window Visual Transformer Abstract: Recently, a surge of interest in visual transformers is to reduce the computational cost by limiting the calculation of self-attention to a local window. Most current work uses a fixed single-scale window for modeling by default, ignoring the impact of... |
Title: Transformer Compressed Sensing via Global Image Tokens Abstract: Convolutional neural networks (CNN) have demonstrated outstanding Compressed Sensing (CS) performance compared to traditional, hand-crafted methods. However, they are broadly limited in terms of generalisability, inductive bias and difficulty to mo... |
Title: Kullback-Leibler control for discrete-time nonlinear systems on continuous spaces Abstract: Kullback-Leibler (KL) control enables efficient numerical methods for nonlinear optimal control problems. The crucial assumption of KL control is the full controllability of the transition distribution. However, this assu... |
Title: Multilingual CheckList: Generation and Evaluation Abstract: The recently proposed CheckList (Riberio et al,. 2020) approach to evaluation of NLP systems has revealed high failure rates for basic capabilities for multiple state-of-the-art and commercial models. However, the CheckList creation process is manual wh... |
Title: DyRep: Bootstrapping Training with Dynamic Re-parameterization Abstract: Structural re-parameterization (Rep) methods achieve noticeable improvements on simple VGG-style networks. Despite the prevalence, current Rep methods simply re-parameterize all operations into an augmented network, including those that rar... |
Title: Can Unsupervised Knowledge Transfer from Social Discussions Help Argument Mining? Abstract: Identifying argument components from unstructured texts and predicting the relationships expressed among them are two primary steps of argument mining. The intrinsic complexity of these tasks demands powerful learning mod... |
Title: Mono vs Multilingual BERT: A Case Study in Hindi and Marathi Named Entity Recognition Abstract: Named entity recognition (NER) is the process of recognising and classifying important information (entities) in text. Proper nouns, such as a person's name, an organization's name, or a location's name, are examples ... |
Title: NPC: Neuron Path Coverage via Characterizing Decision Logic of Deep Neural Networks Abstract: Deep learning has recently been widely applied to many applications across different domains, e.g., image classification and audio recognition. However, the quality of Deep Neural Networks (DNNs) still raises concerns i... |
Title: Learning Dense Correspondence from Synthetic Environments Abstract: Estimation of human shape and pose from a single image is a challenging task. It is an even more difficult problem to map the identified human shape onto a 3D human model. Existing methods map manually labelled human pixels in real 2D images ont... |
Title: Rubik's Cube Operator: A Plug And Play Permutation Module for Better Arranging High Dimensional Industrial Data in Deep Convolutional Processes Abstract: The convolutional neural network (CNN) has been widely applied to process the industrial data based tensor input, which integrates data records of distributed ... |
Title: Horizon-Free Reinforcement Learning in Polynomial Time: the Power of Stationary Policies Abstract: This paper gives the first polynomial-time algorithm for tabular Markov Decision Processes (MDP) that enjoys a regret bound \emph{independent on the planning horizon}. Specifically, we consider tabular MDP with $S$... |
Title: TCN Mapping Optimization for Ultra-Low Power Time-Series Edge Inference Abstract: Temporal Convolutional Networks (TCNs) are emerging lightweight Deep Learning models for Time Series analysis. We introduce an automated exploration approach and a library of optimized kernels to map TCNs on Parallel Ultra-Low Powe... |
Title: Bioformers: Embedding Transformers for Ultra-Low Power sEMG-based Gesture Recognition Abstract: Human-machine interaction is gaining traction in rehabilitation tasks, such as controlling prosthetic hands or robotic arms. Gesture recognition exploiting surface electromyographic (sEMG) signals is one of the most p... |
Title: mcBERT: Momentum Contrastive Learning with BERT for Zero-Shot Slot Filling Abstract: Zero-shot slot filling has received considerable attention to cope with the problem of limited available data for the target domain. One of the important factors in zero-shot learning is to make the model learn generalized and r... |
Title: Personalized incentives as feedback design in generalized Nash equilibrium problems Abstract: We investigate both stationary and time-varying, nonmonotone generalized Nash equilibrium problems that exhibit symmetric interactions among the agents, which are known to be potential. As may happen in practical cases,... |
Title: Knowledge Removal in Sampling-based Bayesian Inference Abstract: The right to be forgotten has been legislated in many countries, but its enforcement in the AI industry would cause unbearable costs. When single data deletion requests come, companies may need to delete the whole models learned with massive resour... |
Title: Extended critical regimes of deep neural networks Abstract: Deep neural networks (DNNs) have been successfully applied to many real-world problems, but a complete understanding of their dynamical and computational principles is still lacking. Conventional theoretical frameworks for analysing DNNs often assume ra... |
Title: Effective Explanations for Entity Resolution Models Abstract: Entity resolution (ER) aims at matching records that refer to the same real-world entity. Although widely studied for the last 50 years, ER still represents a challenging data management problem, and several recent works have started to investigate th... |
Title: A Deep-Discrete Learning Framework for Spherical Surface Registration Abstract: Cortical surface registration is a fundamental tool for neuroimaging analysis that has been shown to improve the alignment of functional regions relative to volumetric approaches. Classically, image registration is performed by optim... |
Title: Using Orientation to Distinguish Overlapping Chromosomes Abstract: A difficult step in the process of karyotyping is segmenting chromosomes that touch or overlap. In an attempt to automate the process, previous studies turned to Deep Learning methods, with some formulating the task as a semantic segmentation pro... |
Title: Interpretable Prediction of Pulmonary Hypertension in Newborns using Echocardiograms Abstract: Pulmonary hypertension (PH) in newborns and infants is a complex condition associated with several pulmonary, cardiac, and systemic diseases contributing to morbidity and mortality. Therefore, accurate and early detect... |
Title: SwiftAgg+: Achieving Asymptotically Optimal Communication Loads in Secure Aggregation for Federated Learning Abstract: We propose SwiftAgg+, a novel secure aggregation protocol for federated learning systems, where a central server aggregates local models of $N \in \mathbb{N}$ distributed users, each of size $L ... |
Title: The Dutch Draw: Constructing a Universal Baseline for Binary Prediction Models Abstract: Novel prediction methods should always be compared to a baseline to know how well they perform. Without this frame of reference, the performance score of a model is basically meaningless. What does it mean when a model achie... |
Title: Locally Asynchronous Stochastic Gradient Descent for Decentralised Deep Learning Abstract: Distributed training algorithms of deep neural networks show impressive convergence speedup properties on very large problems. However, they inherently suffer from communication related slowdowns and communication topology... |
Title: HiFi++: a Unified Framework for Neural Vocoding, Bandwidth Extension and Speech Enhancement Abstract: Generative adversarial networks have recently demonstrated outstanding performance in neural vocoding outperforming best autoregressive and flow-based models. In this paper, we show that this success can be exte... |
Title: Introducing Neural Bag of Whole-Words with ColBERTer: Contextualized Late Interactions using Enhanced Reduction Abstract: Recent progress in neural information retrieval has demonstrated large gains in effectiveness, while often sacrificing the efficiency and interpretability of the neural model compared to clas... |
Title: Explainable Artificial Intelligence for Exhaust Gas Temperature of Turbofan Engines Abstract: Data-driven modeling is an imperative tool in various industrial applications, including many applications in the sectors of aeronautics and commercial aviation. These models are in charge of providing key insights, suc... |
Title: Position Tracking using Likelihood Modeling of Channel Features with Gaussian Processes Abstract: Recent localization frameworks exploit spatial information of complex channel measurements (CMs) to estimate accurate positions even in multipath propagation scenarios. State-of-the art CM fingerprinting(FP)-based m... |
Title: Make-A-Scene: Scene-Based Text-to-Image Generation with Human Priors Abstract: Recent text-to-image generation methods provide a simple yet exciting conversion capability between text and image domains. While these methods have incrementally improved the generated image fidelity and text relevancy, several pivot... |
Title: DPST: De Novo Peptide Sequencing with Amino-Acid-Aware Transformers Abstract: De novo peptide sequencing aims to recover amino acid sequences of a peptide from tandem mass spectrometry (MS) data. Existing approaches for de novo analysis enumerate MS evidence for all amino acid classes during inference. It leads ... |
Title: Multi-armed bandits for online optimization of language model pre-training: the use case of dynamic masking Abstract: Transformer-based language models (TLMs) provide state-of-the-art performance in many modern natural language processing applications. TLM training is conducted in two phases. First, the model is... |
Title: Addressing Missing Sources with Adversarial Support-Matching Abstract: When trained on diverse labeled data, machine learning models have proven themselves to be a powerful tool in all facets of society. However, due to budget limitations, deliberate or non-deliberate censorship, and other problems during data c... |
Title: Towards Exemplar-Free Continual Learning in Vision Transformers: an Account of Attention, Functional and Weight Regularization Abstract: In this paper, we investigate the continual learning of Vision Transformers (ViT) for the challenging exemplar-free scenario, with special focus on how to efficiently distill t... |
Title: Deep Bidirectional Transformers for SoC Flow Specification Mining Abstract: High-quality system-level message flow specifications can lead to comprehensive validation of system-on-chip (SoC) designs. We propose a disruptive method that utilizes an attention mechanism to produce accurate flow specifications from ... |
Title: Improving Maximum Likelihood Difference Scaling method to measure inter content scale Abstract: The goal of most subjective studies is to place a set of stimuli on a perceptual scale. This is mostly done directly by rating, e.g. using single or double stimulus methodologies, or indirectly by ranking or pairwise ... |
Title: GEMA: An open-source Python library for self-organizing-maps Abstract: Organizations have realized the importance of data analysis and its benefits. This in combination with Machine Learning algorithms has allowed to solve problems more easily, making these processes less time-consuming. Neural networks are the ... |
Title: Constrained Parameter Inference as a Principle for Learning Abstract: Learning in biological and artificial neural networks is often framed as a problem in which targeted error signals are used to directly guide parameter updating for more optimal network behaviour. Backpropagation of error (BP) is an example of... |
Title: Decouple-and-Sample: Protecting sensitive information in task agnostic data release Abstract: We propose sanitizer, a framework for secure and task-agnostic data release. While releasing datasets continues to make a big impact in various applications of computer vision, its impact is mostly realized when data sh... |
Title: Supervised Training of Siamese Spiking Neural Networks with Earth Mover's Distance Abstract: This study adapts the highly-versatile siamese neural network model to the event data domain. We introduce a supervised training framework for optimizing Earth Mover's Distance (EMD) between spike trains with spiking neu... |
Title: Distributionally Robust Optimization via Ball Oracle Acceleration Abstract: We develop and analyze algorithms for distributionally robust optimization (DRO) of convex losses. In particular, we consider group-structured and bounded $f$-divergence uncertainty sets. Our approach relies on an accelerated method that... |
Title: Token Dropping for Efficient BERT Pretraining Abstract: Transformer-based models generally allocate the same amount of computation for each token in a given sequence. We develop a simple but effective "token dropping" method to accelerate the pretraining of transformer models, such as BERT, without degrading its... |
Title: Pastiche Master: Exemplar-Based High-Resolution Portrait Style Transfer Abstract: Recent studies on StyleGAN show high performance on artistic portrait generation by transfer learning with limited data. In this paper, we explore more challenging exemplar-based high-resolution portrait style transfer by introduci... |
Title: Dexterous Imitation Made Easy: A Learning-Based Framework for Efficient Dexterous Manipulation Abstract: Optimizing behaviors for dexterous manipulation has been a longstanding challenge in robotics, with a variety of methods from model-based control to model-free reinforcement learning having been previously ex... |
Title: Quantum Feature Selection Abstract: In machine learning, fewer features reduce model complexity. Carefully assessing the influence of each input feature on the model quality is therefore a crucial preprocessing step. We propose a novel feature selection algorithm based on a quadratic unconstrained binary optimiz... |
Title: Precipitaion Nowcasting using Deep Neural Network Abstract: Precipitation nowcasting is of great importance for weather forecast users, for activities ranging from outdoor activities and sports competitions to airport traffic management. In contrast to long-term precipitation forecasts which are traditionally ob... |
Title: Shoring Up the Foundations: Fusing Model Embeddings and Weak Supervision Abstract: Foundation models offer an exciting new paradigm for constructing models with out-of-the-box embeddings and a few labeled examples. However, it is not clear how to best apply foundation models without labeled data. A potential app... |
Title: On Exploiting Layerwise Gradient Statistics for Effective Training of Deep Neural Networks Abstract: Adam and AdaBelief compute and make use of elementwise adaptive stepsizes in training deep neural networks (DNNs) by tracking the exponential moving average (EMA) of the squared-gradient g_t^2 and the squared pre... |
Title: A Manifold View of Adversarial Risk Abstract: The adversarial risk of a machine learning model has been widely studied. Most previous works assume that the data lies in the whole ambient space. We propose to take a new angle and take the manifold assumption into consideration. Assuming data lies in a manifold, w... |
Title: Local optimisation of Nystr\"om samples through stochastic gradient descent Abstract: We study a relaxed version of the column-sampling problem for the Nystr\"om approximation of kernel matrices, where approximations are defined from multisets of landmark points in the ambient space; such multisets are referred ... |
Title: Continuous-Time Audiovisual Fusion with Recurrence vs. Attention for In-The-Wild Affect Recognition Abstract: In this paper, we present our submission to 3rd Affective Behavior Analysis in-the-wild (ABAW) challenge. Learningcomplex interactions among multimodal sequences is critical to recognise dimensional affe... |
Title: Learning Spatiotemporal Chaos Using Next-Generation Reservoir Computing Abstract: Forecasting the behavior of high-dimensional dynamical systems using machine learning requires efficient methods to learn the underlying physical model. We demonstrate spatiotemporal chaos prediction using a machine learning archit... |
Title: Mix and Match: Learning-free Controllable Text Generation using Energy Language Models Abstract: Recent work on controlled text generation has either required attribute-based fine-tuning of the base language model (LM), or has restricted the parameterization of the attribute discriminator to be compatible with t... |
Title: Tackling Online One-Class Incremental Learning by Removing Negative Contrasts Abstract: Recent work studies the supervised online continual learning setting where a learner receives a stream of data whose class distribution changes over time. Distinct from other continual learning settings the learner is present... |
Title: Human Gait Recognition Using Bag of Words Feature Representation Method Abstract: In this paper, we propose a novel gait recognition method based on a bag-of-words feature representation method. The algorithm is trained, tested and evaluated on a unique human gait data consisting of 93 individuals who walked wit... |
Title: Remember and Forget Experience Replay for Multi-Agent Reinforcement Learning Abstract: We present the extension of the Remember and Forget for Experience Replay (ReF-ER) algorithm to Multi-Agent Reinforcement Learning (MARL). {ReF-ER} was shown to outperform state of the art algorithms for continuous control in ... |
Title: Addressing Client Drift in Federated Continual Learning with Adaptive Optimization Abstract: Federated learning has been extensively studied and is the prevalent method for privacy-preserving distributed learning in edge devices. Correspondingly, continual learning is an emerging field targeted towards learning ... |
Title: Text to Mesh Without 3D Supervision Using Limit Subdivision Abstract: We present a technique for zero-shot generation of a 3D model using only a target text prompt. Without a generative model or any 3D supervision our method deforms a control shape of a limit subdivided surface along with a texture map and norma... |
Title: Leveraging unsupervised and weakly-supervised data to improve direct speech-to-speech translation Abstract: End-to-end speech-to-speech translation (S2ST) without relying on intermediate text representations is a rapidly emerging frontier of research. Recent works have demonstrated that the performance of such d... |
Title: Linking Emergent and Natural Languages via Corpus Transfer Abstract: The study of language emergence aims to understand how human languages are shaped by perceptual grounding and communicative intent. Computational approaches to emergent communication (EC) predominantly consider referential games in limited doma... |
Title: Does human speech follow Benford's Law? Abstract: Researchers have observed that the frequencies of leading digits in many man-made and naturally occurring datasets follow a logarithmic curve, with digits that start with the number 1 accounting for $\sim 30\%$ of all numbers in the dataset and digits that start ... |
Title: Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5) Abstract: For a long period, different recommendation tasks typically require designing task-specific architectures and training objectives. As a result, it is hard to transfer the learned knowledge and r... |
Title: Deep reinforcement learning for optimal well control in subsurface systems with uncertain geology Abstract: A general control policy framework based on deep reinforcement learning (DRL) is introduced for closed-loop decision making in subsurface flow settings. Traditional closed-loop modeling workflows in this c... |
Title: Statistic Selection and MCMC for Differentially Private Bayesian Estimation Abstract: This paper concerns differentially private Bayesian estimation of the parameters of a population distribution, when a statistic of a sample from that population is shared in noise to provide differential privacy. This work main... |
Title: Email Summarization to Assist Users in Phishing Identification Abstract: Cyber-phishing attacks recently became more precise, targeted, and tailored by training data to activate only in the presence of specific information or cues. They are adaptable to a much greater extent than traditional phishing detection. ... |
Title: Probing Representation Forgetting in Supervised and Unsupervised Continual Learning Abstract: Continual Learning research typically focuses on tackling the phenomenon of catastrophic forgetting in neural networks. Catastrophic forgetting is associated with an abrupt loss of knowledge previously learned by a mode... |
Title: Automated Algorithm Selection: from Feature-Based to Feature-Free Approaches Abstract: We propose a novel technique for algorithm-selection, applicable to optimisation domains in which there is implicit sequential information encapsulated in the data, e.g., in online bin-packing. Specifically we train two types ... |
Title: Qualitative neural network approximation over R and C: Elementary proofs for analytic and polynomial activation Abstract: In this article, we prove approximation theorems in classes of deep and shallow neural networks with analytic activation functions by elementary arguments. We prove for both real and complex ... |
Title: Reshaping Robot Trajectories Using Natural Language Commands: A Study of Multi-Modal Data Alignment Using Transformers Abstract: Natural language is the most intuitive medium for us to interact with other people when expressing commands and instructions. However, using language is seldom an easy task when humans... |
Title: Amortized Projection Optimization for Sliced Wasserstein Generative Models Abstract: Seeking informative projecting directions has been an important task in utilizing sliced Wasserstein distance in applications. However, finding these directions usually requires an iterative optimization procedure over the space... |
Title: Learning Losses for Strategic Classification Abstract: Strategic classification, i.e. classification under possible strategic manipulations of features, has received a lot of attention from both the machine learning and the game theory community. Most works focus on analysing properties of the optimal decision r... |
Title: Modeling Attrition in Recommender Systems with Departing Bandits Abstract: Traditionally, when recommender systems are formalized as multi-armed bandits, the policy of the recommender system influences the rewards accrued, but not the length of interaction. However, in real-world systems, dissatisfied users may ... |
Title: Dealing with Sparse Rewards Using Graph Neural Networks Abstract: Deep reinforcement learning in partially observable environments is a difficult task in itself, and can be further complicated by a sparse reward signal. Most tasks involving navigation in three-dimensional environments provide the agent with extr... |
Title: Risk-Aware Off-Road Navigation via a Learned Speed Distribution Map Abstract: Motion planning in off-road environments requires reasoning about both the geometry and semantics of the scene (e.g., a robot may be able to drive through soft bushes but not a fallen log). In many recent works, the world is classified... |
Title: Nash Neural Networks : Inferring Utilities from Optimal Behaviour Abstract: We propose Nash Neural Networks ($N^3$) as a new type of Physics Informed Neural Network that is able to infer the underlying utility from observations of how rational individuals behave in a differential game with a Nash equilibrium. We... |
Title: 3D GAN Inversion for Controllable Portrait Image Animation Abstract: Millions of images of human faces are captured every single day; but these photographs portray the likeness of an individual with a fixed pose, expression, and appearance. Portrait image animation enables the post-capture adjustment of these at... |
Title: Randomized Policy Optimization for Optimal Stopping Abstract: Optimal stopping is the problem of determining when to stop a stochastic system in order to maximize reward, which is of practical importance in domains such as finance, operations management and healthcare. Existing methods for high-dimensional optim... |
Title: A Comparative Evaluation of Machine Learning Algorithms for the Prediction of R/C Buildings' Seismic Damage Abstract: Seismic assessment of buildings and determination of their structural damage is at the forefront of modern scientific research. Since now, several researchers have proposed a number of procedures... |
Title: A Comparative Survey of Deep Active Learning Abstract: Active Learning (AL) is a set of techniques for reducing labeling cost by sequentially selecting data samples from a large unlabeled data pool for labeling. Meanwhile, Deep Learning (DL) is data-hungry, and the performance of DL models scales monotonically w... |
Title: A Unified Contrastive Energy-based Model for Understanding the Generative Ability of Adversarial Training Abstract: Adversarial Training (AT) is known as an effective approach to enhance the robustness of deep neural networks. Recently researchers notice that robust models with AT have good generative ability an... |
Title: Chaos is a Ladder: A New Theoretical Understanding of Contrastive Learning via Augmentation Overlap Abstract: Recently, contrastive learning has risen to be a promising approach for large-scale self-supervised learning. However, theoretical understanding of how it works is still unclear. In this paper, we propos... |
Title: From MIM-Based GAN to Anomaly Detection:Event Probability Influence on Generative Adversarial Networks Abstract: In order to introduce deep learning technologies into anomaly detection, Generative Adversarial Networks (GANs) are considered as important roles in the algorithm design and realistic applications. In... |
Title: Non-Probability Sampling Network for Stochastic Human Trajectory Prediction Abstract: Capturing multimodal natures is essential for stochastic pedestrian trajectory prediction, to infer a finite set of future trajectories. The inferred trajectories are based on observation paths and the latent vectors of potenti... |
Title: A Conversational Paradigm for Program Synthesis Abstract: Program synthesis strives to generate a computer program as a solution to a given problem specification. We propose a conversational program synthesis approach via large language models, which addresses the challenges of searching over a vast program spac... |
Title: MKQ-BERT: Quantized BERT with 4-bits Weights and Activations Abstract: Recently, pre-trained Transformer based language models, such as BERT, have shown great superiority over the traditional methods in many Natural Language Processing (NLP) tasks. However, the computational cost for deploying these models is pr... |
Title: Machine-Learning Based Objective Function Selection for Community Detection Abstract: NECTAR, a Node-centric ovErlapping Community deTection AlgoRithm, presented in 2016 by Cohen et. al, chooses dynamically between two objective functions which function to optimize, based on the network on which it is invoked. T... |
Title: Supplemental Material: Lifelong Generative Modelling Using Dynamic Expansion Graph Model Abstract: In this article, we provide the appendix for Lifelong Generative Modelling Using Dynamic Expansion Graph Model. This appendix includes additional visual results as well as the numerical results on the challenging d... |
Title: Analysis of the Production Strategy of Mask Types in the COVID-19 Environment Abstract: Since the outbreak of the COVID-19 in December 2019, medical protective equipment such as disposable medical masks and KN95 masks have become essential resources for the public. Enterprises in all sectors of society have also... |
Title: BDDM: Bilateral Denoising Diffusion Models for Fast and High-Quality Speech Synthesis Abstract: Diffusion probabilistic models (DPMs) and their extensions have emerged as competitive generative models yet confront challenges of efficient sampling. We propose a new bilateral denoising diffusion model (BDDM) that ... |
Title: Sparse Federated Learning with Hierarchical Personalized Models Abstract: Federated learning (FL) can achieve privacy-safe and reliable collaborative training without collecting users' private data. Its excellent privacy security potential promotes a wide range of FL applications in Internet-of-Things (IoT), wir... |
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