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Title: Pre-RTL DNN Hardware Evaluator With Fused Layer Support Abstract: With the popularity of the deep neural network (DNN), hardware accelerators are demanded for real time execution. However, lengthy design process and fast evolving DNN models make hardware evaluation hard to meet the time to market need. This pape... |
Title: Self-focusing virtual screening with active design space pruning Abstract: High-throughput virtual screening is an indispensable technique utilized in the discovery of small molecules. In cases where the library of molecules is exceedingly large, the cost of an exhaustive virtual screen may be prohibitive. Model... |
Title: B\'ezier Curve Gaussian Processes Abstract: Probabilistic models for sequential data are the basis for a variety of applications concerned with processing timely ordered information. The predominant approach in this domain is given by neural networks, which incorporate either stochastic units or components. This... |
Title: XLTime: A Cross-Lingual Knowledge Transfer Framework for Temporal Expression Extraction Abstract: Temporal Expression Extraction (TEE) is essential for understanding time in natural language. It has applications in Natural Language Processing (NLP) tasks such as question answering, information retrieval, and cau... |
Title: Differentiable Simulation of Soft Multi-body Systems Abstract: We present a method for differentiable simulation of soft articulated bodies. Our work enables the integration of differentiable physical dynamics into gradient-based pipelines. We develop a top-down matrix assembly algorithm within Projective Dynami... |
Title: Explain and Conquer: Personalised Text-based Reviews to Achieve Transparency Abstract: There are many contexts where dyadic data is present. Social networking is a well-known example, where transparency has grown on importance. In these contexts, pairs of items are linked building a network where interactions pl... |
Title: The ICML 2022 Expressive Vocalizations Workshop and Competition: Recognizing, Generating, and Personalizing Vocal Bursts Abstract: The ICML Expressive Vocalization (ExVo) Competition is focused on understanding and generating vocal bursts: laughs, gasps, cries, and other non-verbal vocalizations that are central... |
Title: Do More Negative Samples Necessarily Hurt in Contrastive Learning? Abstract: Recent investigations in noise contrastive estimation suggest, both empirically as well as theoretically, that while having more "negative samples" in the contrastive loss improves downstream classification performance initially, beyond... |
Title: Meta-Cognition. An Inverse-Inverse Reinforcement Learning Approach for Cognitive Radars Abstract: This paper considers meta-cognitive radars in an adversarial setting. A cognitive radar optimally adapts its waveform (response) in response to maneuvers (probes) of a possibly adversarial moving target. A meta-cogn... |
Title: Synthesized Speech Detection Using Convolutional Transformer-Based Spectrogram Analysis Abstract: Synthesized speech is common today due to the prevalence of virtual assistants, easy-to-use tools for generating and modifying speech signals, and remote work practices. Synthesized speech can also be used for nefar... |
Title: Splicing Detection and Localization In Satellite Imagery Using Conditional GANs Abstract: The widespread availability of image editing tools and improvements in image processing techniques allow image manipulation to be very easy. Oftentimes, easy-to-use yet sophisticated image manipulation tools yields distorti... |
Title: Frequency Domain-Based Detection of Generated Audio Abstract: Attackers may manipulate audio with the intent of presenting falsified reports, changing an opinion of a public figure, and winning influence and power. The prevalence of inauthentic multimedia continues to rise, so it is imperative to develop a set o... |
Title: Assessing Dataset Bias in Computer Vision Abstract: A biased dataset is a dataset that generally has attributes with an uneven class distribution. These biases have the tendency to propagate to the models that train on them, often leading to a poor performance in the minority class. In this project, we will expl... |
Title: Diverse Image Captioning with Grounded Style Abstract: Stylized image captioning as presented in prior work aims to generate captions that reflect characteristics beyond a factual description of the scene composition, such as sentiments. Such prior work relies on given sentiment identifiers, which are used to ex... |
Title: i-Code: An Integrative and Composable Multimodal Learning Framework Abstract: Human intelligence is multimodal; we integrate visual, linguistic, and acoustic signals to maintain a holistic worldview. Most current pretraining methods, however, are limited to one or two modalities. We present i-Code, a self-superv... |
Title: Zero-shot Sonnet Generation with Discourse-level Planning and Aesthetics Features Abstract: Poetry generation, and creative language generation in general, usually suffers from the lack of large training data. In this paper, we present a novel framework to generate sonnets that does not require training on poems... |
Title: AmbiPun: Generating Humorous Puns with Ambiguous Context Abstract: In this paper, we propose a simple yet effective way to generate pun sentences that does not require any training on existing puns. Our approach is inspired by humor theories that ambiguity comes from the context rather than the pun word itself. ... |
Title: Optimizing Mixture of Experts using Dynamic Recompilations Abstract: The Mixture of Experts architecture allows for outrageously large neural networks by scaling model parameter size independently from computational demand (FLOPs). However, current DNN frameworks cannot effectively support the dynamic data flow ... |
Title: SMLT: A Serverless Framework for Scalable and Adaptive Machine Learning Design and Training Abstract: In today's production machine learning (ML) systems, models are continuously trained, improved, and deployed. ML design and training are becoming a continuous workflow of various tasks that have dynamic resource... |
Title: DeeptDCS: Deep Learning-Based Estimation of Currents Induced During Transcranial Direct Current Stimulation Abstract: Objective: Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation technique used to generate conduction currents in the head and disrupt brain functions. To rapidly ev... |
Title: Provably Confidential Language Modelling Abstract: Large language models are shown to memorize privacy information such as social security numbers in training data. Given the sheer scale of the training corpus, it is challenging to screen and filter these privacy data, either manually or automatically. In this p... |
Title: Machine Learning based Framework for Robust Price-Sensitivity Estimation with Application to Airline Pricing Abstract: We consider the problem of dynamic pricing of a product in the presence of feature-dependent price sensitivity. Based on the Poisson semi-parametric approach, we construct a flexible yet interpr... |
Title: fairlib: A Unified Framework for Assessing and Improving Classification Fairness Abstract: This paper presents fairlib, an open-source framework for assessing and improving classification fairness. It provides a systematic framework for quickly reproducing existing baseline models, developing new methods, evalua... |
Title: Uncertainty estimation of pedestrian future trajectory using Bayesian approximation Abstract: Past research on pedestrian trajectory forecasting mainly focused on deterministic predictions which provide only point estimates of future states. These future estimates can help an autonomous vehicle plan its trajecto... |
Title: Crystal Twins: Self-supervised Learning for Crystalline Material Property Prediction Abstract: Machine learning (ML) models have been widely successful in the prediction of material properties. However, large labeled datasets required for training accurate ML models are elusive and computationally expensive to g... |
Title: Virtual Analog Modeling of Distortion Circuits Using Neural Ordinary Differential Equations Abstract: Recent research in deep learning has shown that neural networks can learn differential equations governing dynamical systems. In this paper, we adapt this concept to Virtual Analog (VA) modeling to learn the ord... |
Title: Spatial-Temporal Meta-path Guided Explainable Crime Prediction Abstract: Exposure to crime and violence can harm individuals' quality of life and the economic growth of communities. In light of the rapid development in machine learning, there is a rise in the need to explore automated solutions to prevent crimes... |
Title: Self-Taught Metric Learning without Labels Abstract: We present a novel self-taught framework for unsupervised metric learning, which alternates between predicting class-equivalence relations between data through a moving average of an embedding model and learning the model with the predicted relations as pseudo... |
Title: ASE: Large-Scale Reusable Adversarial Skill Embeddings for Physically Simulated Characters Abstract: The incredible feats of athleticism demonstrated by humans are made possible in part by a vast repertoire of general-purpose motor skills, acquired through years of practice and experience. These skills not only ... |
Title: Modeling Task Interactions in Document-Level Joint Entity and Relation Extraction Abstract: We target on the document-level relation extraction in an end-to-end setting, where the model needs to jointly perform mention extraction, coreference resolution (COREF) and relation extraction (RE) at once, and gets eval... |
Title: Generalized Knowledge Distillation via Relationship Matching Abstract: The knowledge of a well-trained deep neural network (a.k.a. the "teacher") is valuable for learning similar tasks. Knowledge distillation extracts knowledge from the teacher and integrates it with the target model (a.k.a. the "student"), whic... |
Title: CoCa: Contrastive Captioners are Image-Text Foundation Models Abstract: Exploring large-scale pretrained foundation models is of significant interest in computer vision because these models can be quickly transferred to many downstream tasks. This paper presents Contrastive Captioner (CoCa), a minimalist design ... |
Title: Second Order Path Variationals in Non-Stationary Online Learning Abstract: We consider the problem of universal dynamic regret minimization under exp-concave and smooth losses. We show that appropriately designed Strongly Adaptive algorithms achieve a dynamic regret of $\tilde O(d^2 n^{1/5} C_n^{2/5} \vee d^2)$,... |
Title: Zero-Episode Few-Shot Contrastive Predictive Coding: Solving intelligence tests without prior training Abstract: Video prediction models often combine three components: an encoder from pixel space to a small latent space, a latent space prediction model, and a generative model back to pixel space. However, the l... |
Title: Probabilistic Symmetry for Improved Trajectory Forecasting Abstract: Trajectory prediction is a core AI problem with broad applications in robotics and autonomous driving. While most existing works focus on deterministic prediction, producing probabilistic forecasts to quantify prediction uncertainty is critical... |
Title: Explain to Not Forget: Defending Against Catastrophic Forgetting with XAI Abstract: The ability to continuously process and retain new information like we do naturally as humans is a feat that is highly sought after when training neural networks. Unfortunately, the traditional optimization algorithms often requi... |
Title: Self-supervised learning unveils morphological clusters behind lung cancer types and prognosis Abstract: Histopathological images of tumors contain abundant information about how tumors grow and how they interact with their micro-environment. Characterizing and improving our understanding of phenotypes could rev... |
Title: DeepFD: Automated Fault Diagnosis and Localization for Deep Learning Programs Abstract: As Deep Learning (DL) systems are widely deployed for mission-critical applications, debugging such systems becomes essential. Most existing works identify and repair suspicious neurons on the trained Deep Neural Network (DNN... |
Title: Towards Theoretical Analysis of Transformation Complexity of ReLU DNNs Abstract: This paper aims to theoretically analyze the complexity of feature transformations encoded in DNNs with ReLU layers. We propose metrics to measure three types of complexities of transformations based on the information theory. We fu... |
Title: Uncertainty-Autoencoder-Based Privacy and Utility Preserving Data Type Conscious Transformation Abstract: We propose an adversarial learning framework that deals with the privacy-utility tradeoff problem under two types of conditions: data-type ignorant, and data-type aware. Under data-type aware conditions, the... |
Title: Word Tour: One-dimensional Word Embeddings via the Traveling Salesman Problem Abstract: Word embeddings are one of the most fundamental technologies used in natural language processing. Existing word embeddings are high-dimensional and consume considerable computational resources. In this study, we propose WordT... |
Title: State Representation Learning for Goal-Conditioned Reinforcement Learning Abstract: This paper presents a novel state representation for reward-free Markov decision processes. The idea is to learn, in a self-supervised manner, an embedding space where distances between pairs of embedded states correspond to the ... |
Title: Nonstationary Bandit Learning via Predictive Sampling Abstract: Although Thompson sampling is widely used in stationary environments, it does not effectively account for nonstationarities. To address this limitation, we propose predictive sampling, a policy that balances between exploration and exploitation in n... |
Title: Sequencer: Deep LSTM for Image Classification Abstract: In recent computer vision research, the advent of the Vision Transformer (ViT) has rapidly revolutionized various architectural design efforts: ViT achieved state-of-the-art image classification performance using self-attention found in natural language pro... |
Title: The Isabelle ENIGMA Abstract: We significantly improve the performance of the E automated theorem prover on the Isabelle Sledgehammer problems by combining learning and theorem proving in several ways. In particular, we develop targeted versions of the ENIGMA guidance for the Isabelle problems, targeted versions... |
Title: Lifelong Ensemble Learning based on Multiple Representations for Few-Shot Object Recognition Abstract: Service robots are integrating more and more into our daily lives to help us with various tasks. In such environments, robots frequently face new objects while working in the environment and need to learn them ... |
Title: Modelling calibration uncertainty in networks of environmental sensors Abstract: Networks of low-cost sensors are becoming ubiquitous, but often suffer from poor accuracies and drift. Regular colocation with reference sensors allows recalibration but is complicated and expensive. Alternatively the calibration ca... |
Title: Wild Patterns Reloaded: A Survey of Machine Learning Security against Training Data Poisoning Abstract: The success of machine learning is fueled by the increasing availability of computing power and large training datasets. The training data is used to learn new models or update existing ones, assuming that it ... |
Title: EmoBank: Studying the Impact of Annotation Perspective and Representation Format on Dimensional Emotion Analysis Abstract: We describe EmoBank, a corpus of 10k English sentences balancing multiple genres, which we annotated with dimensional emotion metadata in the Valence-Arousal-Dominance (VAD) representation f... |
Title: On Continual Model Refinement in Out-of-Distribution Data Streams Abstract: Real-world natural language processing (NLP) models need to be continually updated to fix the prediction errors in out-of-distribution (OOD) data streams while overcoming catastrophic forgetting. However, existing continual learning (CL)... |
Title: A Manifold Two-Sample Test Study: Integral Probability Metric with Neural Networks Abstract: Two-sample tests are important areas aiming to determine whether two collections of observations follow the same distribution or not. We propose two-sample tests based on integral probability metric (IPM) for high-dimens... |
Title: Few-Shot Document-Level Relation Extraction Abstract: We present FREDo, a few-shot document-level relation extraction (FSDLRE) benchmark. As opposed to existing benchmarks which are built on sentence-level relation extraction corpora, we argue that document-level corpora provide more realism, particularly regard... |
Title: Exploring Rawlsian Fairness for K-Means Clustering Abstract: We conduct an exploratory study that looks at incorporating John Rawls' ideas on fairness into existing unsupervised machine learning algorithms. Our focus is on the task of clustering, specifically the k-means clustering algorithm. To the best of our ... |
Title: SVTS: Scalable Video-to-Speech Synthesis Abstract: Video-to-speech synthesis (also known as lip-to-speech) refers to the translation of silent lip movements into the corresponding audio. This task has received an increasing amount of attention due to its self-supervised nature (i.e., can be trained without manua... |
Title: Hypercomplex Image-to-Image Translation Abstract: Image-to-image translation (I2I) aims at transferring the content representation from an input domain to an output one, bouncing along different target domains. Recent I2I generative models, which gain outstanding results in this task, comprise a set of diverse d... |
Title: Learning Abstract and Transferable Representations for Planning Abstract: We are concerned with the question of how an agent can acquire its own representations from sensory data. We restrict our focus to learning representations for long-term planning, a class of problems that state-of-the-art learning methods ... |
Title: Data Cleansing for Indoor Positioning Wi-Fi Fingerprinting Datasets Abstract: Wearable and IoT devices requiring positioning and localisation services grow in number exponentially every year. This rapid growth also produces millions of data entries that need to be pre-processed prior to being used in any indoor ... |
Title: MAD: Self-Supervised Masked Anomaly Detection Task for Multivariate Time Series Abstract: In this paper, we introduce Masked Anomaly Detection (MAD), a general self-supervised learning task for multivariate time series anomaly detection. With the increasing availability of sensor data from industrial systems, be... |
Title: Dynamic Sparse R-CNN Abstract: Sparse R-CNN is a recent strong object detection baseline by set prediction on sparse, learnable proposal boxes and proposal features. In this work, we propose to improve Sparse R-CNN with two dynamic designs. First, Sparse R-CNN adopts a one-to-one label assignment scheme, where t... |
Title: Concept Activation Vectors for Generating User-Defined 3D Shapes Abstract: We explore the interpretability of 3D geometric deep learning models in the context of Computer-Aided Design (CAD). The field of parametric CAD can be limited by the difficulty of expressing high-level design concepts in terms of a few nu... |
Title: Efficient Accelerator for Dilated and Transposed Convolution with Decomposition Abstract: Hardware acceleration for dilated and transposed convolution enables real time execution of related tasks like segmentation, but current designs are specific for these convolutional types or suffer from complex control for ... |
Title: Prediction of fish location by combining fisheries data and sea bottom temperature forecasting Abstract: This paper combines fisheries dependent data and environmental data to be used in a machine learning pipeline to predict the spatio-temporal abundance of two species (plaice and sole) commonly caught by the B... |
Title: Using Deep Reinforcement Learning to solve Optimal Power Flow problem with generator failures Abstract: Deep Reinforcement Learning (DRL) is being used in many domains. One of the biggest advantages of DRL is that it enables the continuous improvement of a learning agent. Secondly, the DRL framework is robust an... |
Title: Improved Orientation Estimation and Detection with Hybrid Object Detection Networks for Automotive Radar Abstract: This paper presents novel hybrid architectures that combine grid- and point-based processing to improve the detection performance and orientation estimation of radar-based object detection networks.... |
Title: Predicting vacant parking space availability zone-wisely: a graph based spatio-temporal prediction approach Abstract: Vacant parking space (VPS) prediction is one of the key issues of intelligent parking guidance systems. Accurately predicting VPS information plays a crucial role in intelligent parking guidance ... |
Title: Axonal Delay As a Short-Term Memory for Feed Forward Deep Spiking Neural Networks Abstract: The information of spiking neural networks (SNNs) are propagated between the adjacent biological neuron by spikes, which provides a computing paradigm with the promise of simulating the human brain. Recent studies have fo... |
Title: Optimizing One-pixel Black-box Adversarial Attacks Abstract: The output of Deep Neural Networks (DNN) can be altered by a small perturbation of the input in a black box setting by making multiple calls to the DNN. However, the high computation and time required makes the existing approaches unusable. This work s... |
Title: Processing Network Controls via Deep Reinforcement Learning Abstract: Novel advanced policy gradient (APG) algorithms, such as proximal policy optimization (PPO), trust region policy optimization, and their variations, have become the dominant reinforcement learning (RL) algorithms because of their ease of imple... |
Title: Accelerating phase-field-based simulation via machine learning Abstract: Phase-field-based models have become common in material science, mechanics, physics, biology, chemistry, and engineering for the simulation of microstructure evolution. Yet, they suffer from the drawback of being computationally very costly... |
Title: The Limits of Word Level Differential Privacy Abstract: As the issues of privacy and trust are receiving increasing attention within the research community, various attempts have been made to anonymize textual data. A significant subset of these approaches incorporate differentially private mechanisms to perturb... |
Title: Domino Saliency Metrics: Improving Existing Channel Saliency Metrics with Structural Information Abstract: Channel pruning is used to reduce the number of weights in a Convolutional Neural Network (CNN). Channel pruning removes slices of the weight tensor so that the convolution layer remains dense. The removal ... |
Title: Dual Cross-Attention Learning for Fine-Grained Visual Categorization and Object Re-Identification Abstract: Recently, self-attention mechanisms have shown impressive performance in various NLP and CV tasks, which can help capture sequential characteristics and derive global information. In this work, we explore ... |
Title: Evaluating Transferability for Covid 3D Localization Using CT SARS-CoV-2 segmentation models Abstract: Recent studies indicate that detecting radiographic patterns on CT scans can yield high sensitivity and specificity for Covid-19 localization. In this paper, we investigate the appropriateness of deep learning ... |
Title: Making SGD Parameter-Free Abstract: We develop an algorithm for parameter-free stochastic convex optimization (SCO) whose rate of convergence is only a double-logarithmic factor larger than the optimal rate for the corresponding known-parameter setting. In contrast, the best previously known rates for parameter-... |
Title: Compound virtual screening by learning-to-rank with gradient boosting decision tree and enrichment-based cumulative gain Abstract: Learning-to-rank, a machine learning technique widely used in information retrieval, has recently been applied to the problem of ligand-based virtual screening, to accelerate the ear... |
Title: Efficient Few-Shot Fine-Tuning for Opinion Summarization Abstract: Abstractive summarization models are typically pre-trained on large amounts of generic texts, then fine-tuned on tens or hundreds of thousands of annotated samples. However, in opinion summarization, large annotated datasets of reviews paired wit... |
Title: Wavelet neural operator: a neural operator for parametric partial differential equations Abstract: With massive advancements in sensor technologies and Internet-of-things, we now have access to terabytes of historical data; however, there is a lack of clarity in how to best exploit the data to predict future eve... |
Title: Semi-Supervised Cascaded Clustering for Classification of Noisy Label Data Abstract: The performance of supervised classification techniques often deteriorates when the data has noisy labels. Even the semi-supervised classification approaches have largely focused only on the problem of handling missing labels. M... |
Title: FEDNEST: Federated Bilevel, Minimax, and Compositional Optimization Abstract: Standard federated optimization methods successfully apply to stochastic problems with single-level structure. However, many contemporary ML problems -- including adversarial robustness, hyperparameter tuning, and actor-critic -- fall ... |
Title: Minimum Cost Intervention Design for Causal Effect Identification Abstract: Pearl's do calculus is a complete axiomatic approach to learn the identifiable causal effects from observational data. When such an effect is not identifiable, it is necessary to perform a collection of often costly interventions in the ... |
Title: Multivariate Prediction Intervals for Random Forests Abstract: Accurate uncertainty estimates can significantly improve the performance of iterative design of experiments, as in Sequential and Reinforcement learning. For many such problems in engineering and the physical sciences, the design task depends on mult... |
Title: Group-Invariant Quantum Machine Learning Abstract: Quantum Machine Learning (QML) models are aimed at learning from data encoded in quantum states. Recently, it has been shown that models with little to no inductive biases (i.e., with no assumptions about the problem embedded in the model) are likely to have tra... |
Title: DeepBayes -- an estimator for parameter estimation in stochastic nonlinear dynamical models Abstract: Stochastic nonlinear dynamical systems are ubiquitous in modern, real-world applications. Yet, estimating the unknown parameters of stochastic, nonlinear dynamical models remains a challenging problem. The major... |
Title: Time Shifts to Reduce the Size of Reservoir Computers Abstract: A reservoir computer is a type of dynamical system arranged to do computation. Typically, a reservoir computer is constructed by connecting a large number of nonlinear nodes in a network that includes recurrent connections. In order to achieve accur... |
Title: Fine-Grained Address Segmentation for Attention-Based Variable-Degree Prefetching Abstract: Machine learning algorithms have shown potential to improve prefetching performance by accurately predicting future memory accesses. Existing approaches are based on the modeling of text prediction, considering prefetchin... |
Title: An Adaptive Incremental Gradient Method With Support for Non-Euclidean Norms Abstract: Stochastic variance reduced methods have shown strong performance in solving finite-sum problems. However, these methods usually require the users to manually tune the step-size, which is time-consuming or even infeasible for ... |
Title: pyRDF2Vec: A Python Implementation and Extension of RDF2Vec Abstract: This paper introduces pyRDF2Vec, a Python software package that reimplements the well-known RDF2Vec algorithm along with several of its extensions. By making the algorithm available in the most popular data science language, and by bundling al... |
Title: Original or Translated? A Causal Analysis of the Impact of Translationese on Machine Translation Performance Abstract: Human-translated text displays distinct features from naturally written text in the same language. This phenomena, known as translationese, has been argued to confound the machine translation (M... |
Title: Machine Learning Operations (MLOps): Overview, Definition, and Architecture Abstract: The final goal of all industrial machine learning (ML) projects is to develop ML products and rapidly bring them into production. However, it is highly challenging to automate and operationalize ML products and thus many ML end... |
Title: Language Models in the Loop: Incorporating Prompting into Weak Supervision Abstract: We propose a new strategy for applying large pre-trained language models to novel tasks when labeled training data is limited. Rather than apply the model in a typical zero-shot or few-shot fashion, we treat the model as the bas... |
Title: Most Activation Functions Can Win the Lottery Without Excessive Depth Abstract: The strong lottery ticket hypothesis has highlighted the potential for training deep neural networks by pruning, which has inspired interesting practical and theoretical insights into how neural networks can represent functions. For ... |
Title: Second-Order Sensitivity Analysis for Bilevel Optimization Abstract: In this work we derive a second-order approach to bilevel optimization, a type of mathematical programming in which the solution to a parameterized optimization problem (the "lower" problem) is itself to be optimized (in the "upper" problem) as... |
Title: Learning Individual Interactions from Population Dynamics with Discrete-Event Simulation Model Abstract: The abundance of data affords researchers to pursue more powerful computational tools to learn the dynamics of complex system, such as neural networks, engineered systems and social networks. Traditional mach... |
Title: Equity and Fairness of Bayesian Knowledge Tracing Abstract: We consider the equity and fairness of curricula derived from Knowledge Tracing models. We begin by defining a unifying notion of an equitable tutoring system as a system that achieves maximum possible knowledge in minimal time for each student interact... |
Title: Knowledge Distillation of Russian Language Models with Reduction of Vocabulary Abstract: Today, transformer language models serve as a core component for majority of natural language processing tasks. Industrial application of such models requires minimization of computation time and memory footprint. Knowledge ... |
Title: Convolutional and Residual Networks Provably Contain Lottery Tickets Abstract: The Lottery Ticket Hypothesis continues to have a profound practical impact on the quest for small scale deep neural networks that solve modern deep learning tasks at competitive performance. These lottery tickets are identified by pr... |
Title: FedSPLIT: One-Shot Federated Recommendation System Based on Non-negative Joint Matrix Factorization and Knowledge Distillation Abstract: Non-negative matrix factorization (NMF) with missing-value completion is a well-known effective Collaborative Filtering (CF) method used to provide personalized user recommenda... |
Title: GitRank: A Framework to Rank GitHub Repositories Abstract: Open-source repositories provide wealth of information and are increasingly being used to build artificial intelligence (AI) based systems to solve problems in software engineering. Open-source repositories could be of varying quality levels, and bad-qua... |
Title: KenSwQuAD -- A Question Answering Dataset for Swahili Low Resource Language Abstract: This research developed a Kencorpus Swahili Question Answering Dataset KenSwQuAD from raw data of Swahili language, which is a low resource language predominantly spoken in Eastern African and also has speakers in other parts o... |
Title: Response Component Analysis for Sea State Estimation Using Artificial Neural Networks and Vessel Response Spectral Data Abstract: The use of the `ship as a wave buoy analogy' (SAWB) provides a novel means to estimate sea states, where relationships are established between causal wave properties and vessel motion... |
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