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Title: A Machine Learning and Computer Vision Approach to Geomagnetic Storm Forecasting Abstract: Geomagnetic storms, disturbances of Earth's magnetosphere caused by masses of charged particles being emitted from the Sun, are an uncontrollable threat to modern technology. Notably, they have the potential to damage sate... |
Title: Cryptocurrency Return Prediction Using Investor Sentiment Extracted by BERT-Based Classifiers from News Articles, Reddit Posts and Tweets Abstract: This paper studies the extent at which investor sentiment contributes to cryptocurrency return prediction. Investor sentiment is extracted from news articles, Reddit... |
Title: Stochastic Multi-armed Bandits with Non-stationary Rewards Generated by a Linear Dynamical System Abstract: The stochastic multi-armed bandit has provided a framework for studying decision-making in unknown environments. We propose a variant of the stochastic multi-armed bandit where the rewards are sampled from... |
Title: Stock Price Prediction using Sentiment Analysis and Deep Learning for Indian Markets Abstract: Stock market prediction has been an active area of research for a considerable period. Arrival of computing, followed by Machine Learning has upgraded the speed of research as well as opened new avenues. As part of thi... |
Title: Surrogate Ensemble Forecasting for Dynamic Climate Impact Models Abstract: As acute climate change impacts weather and climate variability, there is increased demand for robust climate impact model predictions from which forecasts of the impacts can be derived. The quality of those predictions are limited by the... |
Title: Multi-View Breast Cancer Classification via Hypercomplex Neural Networks Abstract: Traditionally, deep learning-based methods for breast cancer classification perform a single-view analysis. However, radiologists simultaneously analyze all four views that compose a mammography exam, owing to the correlations con... |
Title: Variational Heteroscedastic Volatility Model Abstract: We propose Variational Heteroscedastic Volatility Model (VHVM) -- an end-to-end neural network architecture capable of modelling heteroscedastic behaviour in multivariate financial time series. VHVM leverages recent advances in several areas of deep learning... |
Title: MuCoT: Multilingual Contrastive Training for Question-Answering in Low-resource Languages Abstract: Accuracy of English-language Question Answering (QA) systems has improved significantly in recent years with the advent of Transformer-based models (e.g., BERT). These models are pre-trained in a self-supervised f... |
Title: GlacierNet2: A Hybrid Multi-Model Learning Architecture for Alpine Glacier Mapping Abstract: In recent decades, climate change has significantly affected glacier dynamics, resulting in mass loss and an increased risk of glacier-related hazards including supraglacial and proglacial lake development, as well as ca... |
Title: What Language Model Architecture and Pretraining Objective Work Best for Zero-Shot Generalization? Abstract: Large pretrained Transformer language models have been shown to exhibit zero-shot generalization, i.e. they can perform a wide variety of tasks that they were not explicitly trained on. However, the archi... |
Title: Benchmarking Active Learning Strategies for Materials Optimization and Discovery Abstract: Autonomous physical science is revolutionizing materials science. In these systems, machine learning controls experiment design, execution, and analysis in a closed loop. Active learning, the machine learning field of opti... |
Title: The MIT Supercloud Workload Classification Challenge Abstract: High-Performance Computing (HPC) centers and cloud providers support an increasingly diverse set of applications on heterogenous hardware. As Artificial Intelligence (AI) and Machine Learning (ML) workloads have become an increasingly larger share of... |
Title: Generative Negative Replay for Continual Learning Abstract: Learning continually is a key aspect of intelligence and a necessary ability to solve many real-life problems. One of the most effective strategies to control catastrophic forgetting, the Achilles' heel of continual learning, is storing part of the old ... |
Title: Probabilistic Compositional Embeddings for Multimodal Image Retrieval Abstract: Existing works in image retrieval often consider retrieving images with one or two query inputs, which do not generalize to multiple queries. In this work, we investigate a more challenging scenario for composing multiple multimodal ... |
Title: Distributed learning optimisation of Cox models can leak patient data: Risks and solutions Abstract: Medical data are often highly sensitive, and frequently there are missing data. Due to the data's sensitive nature, there is an interest in creating modelling methods where the data are kept in each local centre ... |
Title: Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback Abstract: We apply preference modeling and reinforcement learning from human feedback (RLHF) to finetune language models to act as helpful and harmless assistants. We find this alignment training improves performance on alm... |
Title: A Hierarchical Block Distance Model for Ultra Low-Dimensional Graph Representations Abstract: Graph Representation Learning (GRL) has become central for characterizing structures of complex networks and performing tasks such as link prediction, node classification, network reconstruction, and community detection... |
Title: A DNN Framework for Learning Lagrangian Drift With Uncertainty Abstract: Reconstructions of Lagrangian drift, for example for objects lost at sea, are often uncertain due to unresolved physical phenomena within the data. Uncertainty is usually overcome by introducing stochasticity into the drift, but this approa... |
Title: NARX Identification using Derivative-Based Regularized Neural Networks Abstract: This work presents a novel regularization method for the identification of Nonlinear Autoregressive eXogenous (NARX) models. The regularization method promotes the exponential decay of the influence of past input samples on the curr... |
Title: Offline Distillation for Robot Lifelong Learning with Imbalanced Experience Abstract: Robots will experience non-stationary environment dynamics throughout their lifetime: the robot dynamics can change due to wear and tear, or its surroundings may change over time. Eventually, the robots should perform well in a... |
Title: VisCUIT: Visual Auditor for Bias in CNN Image Classifier Abstract: CNN image classifiers are widely used, thanks to their efficiency and accuracy. However, they can suffer from biases that impede their practical applications. Most existing bias investigation techniques are either inapplicable to general image cl... |
Title: Learning Performance Graphs from Demonstrations via Task-Based Evaluations Abstract: In the learning from demonstration (LfD) paradigm, understanding and evaluating the demonstrated behaviors plays a critical role in extracting control policies for robots. Without this knowledge, a robot may infer incorrect rewa... |
Title: Spatiotemporal Estimation of TROPOMI NO2 Column with Depthwise Partial Convolutional Neural Network Abstract: Satellite-derived measurements are negatively impacted by cloud cover and surface reflectivity. These biases must be discarded and significantly increase the amount of missing data within remote sensing ... |
Title: An Algebraically Converging Stochastic Gradient Descent Algorithm for Global Optimization Abstract: We propose a new stochastic gradient descent algorithm for finding the global optimizer of nonconvex optimization problems, referred to here as "AdaVar". A key component in the algorithm is the adaptive tuning of ... |
Title: Dynamic Dialogue Policy Transformer for Continual Reinforcement Learning Abstract: Continual learning is one of the key components of human learning and a necessary requirement of artificial intelligence. As dialogue can potentially span infinitely many topics and tasks, a task-oriented dialogue system must have... |
Title: Arch-Graph: Acyclic Architecture Relation Predictor for Task-Transferable Neural Architecture Search Abstract: Neural Architecture Search (NAS) aims to find efficient models for multiple tasks. Beyond seeking solutions for a single task, there are surging interests in transferring network design knowledge across... |
Title: Uncertainty-Aware Search Framework for Multi-Objective Bayesian Optimization Abstract: We consider the problem of multi-objective (MO) blackbox optimization using expensive function evaluations, where the goal is to approximate the true Pareto set of solutions while minimizing the number of function evaluations.... |
Title: Maximum Entropy Baseline for Integrated Gradients Abstract: Integrated Gradients (IG), one of the most popular explainability methods available, still remains ambiguous in the selection of baseline, which may seriously impair the credibility of the explanations. This study proposes a new uniform baseline, i.e., ... |
Title: Machine learning predictions for local electronic properties of disordered correlated electron systems Abstract: We present a scalable machine learning (ML) model to predict local electronic properties such as on-site electron number and double occupation for disordered correlated electron systems. Our approach ... |
Title: Automated Surface Texture Analysis via Discrete Cosine Transform and Discrete Wavelet Transform Abstract: Surface roughness and texture are critical to the functional performance of engineering components. The ability to analyze roughness and texture effectively and efficiently is much needed to ensure surface q... |
Title: S-DABT: Schedule and Dependency-Aware Bug Triage in Open-Source Bug Tracking Systems Abstract: Fixing bugs in a timely manner lowers various potential costs in software maintenance. However, manual bug fixing scheduling can be time-consuming, cumbersome, and error-prone. In this paper, we propose the Schedule an... |
Title: Discovering material information using hierarchical Reformer model on financial regulatory filings Abstract: Most applications of machine learning for finance are related to forecasting tasks for investment decisions. Instead, we aim to promote a better understanding of financial markets with machine learning te... |
Title: Comparison Analysis of Traditional Machine Learning and Deep Learning Techniques for Data and Image Classification Abstract: The purpose of the study is to analyse and compare the most common machine learning and deep learning techniques used for computer vision 2D object classification tasks. Firstly, we will p... |
Title: InCoder: A Generative Model for Code Infilling and Synthesis Abstract: Code is seldom written in a single left-to-right pass and is instead repeatedly edited and refined. We introduce InCoder, a unified generative model that can perform program synthesis (via left-to-right generation) as well as editing (via inf... |
Title: L3Cube-MahaNER: A Marathi Named Entity Recognition Dataset and BERT models Abstract: Named Entity Recognition (NER) is a basic NLP task and finds major applications in conversational and search systems. It helps us identify key entities in a sentence used for the downstream application. NER or similar slot filli... |
Title: Local and global topological complexity measures OF ReLU neural network functions Abstract: We apply a generalized piecewise-linear (PL) version of Morse theory due to Grunert-Kuhnel-Rote to define and study new local and global notions of topological complexity for fully-connected feedforward ReLU neural networ... |
Title: Massive MIMO Beam Management in Sub-6 GHz 5G NR Abstract: Beam codebooks are a new feature of massive multiple-input multiple-output (M-MIMO) in 5G new radio (NR). Codebooks comprised of beamforming vectors are used to transmit reference signals and obtain limited channel state information (CSI) from receivers v... |
Title: Slope stability predictions on spatially variable random fields using machine learning surrogate models Abstract: Random field Monte Carlo (MC) reliability analysis is a robust stochastic method to determine the probability of failure. This method, however, requires a large number of numerical simulations demand... |
Title: Highly efficient reliability analysis of anisotropic heterogeneous slopes: Machine Learning aided Monte Carlo method Abstract: Machine Learning (ML) algorithms are increasingly used as surrogate models to increase the efficiency of stochastic reliability analyses in geotechnical engineering. This paper presents ... |
Title: Optimal Membership Inference Bounds for Adaptive Composition of Sampled Gaussian Mechanisms Abstract: Given a trained model and a data sample, membership-inference (MI) attacks predict whether the sample was in the model's training set. A common countermeasure against MI attacks is to utilize differential privac... |
Title: Prediction of motor insurance claims occurrence as an imbalanced machine learning problem Abstract: The insurance industry, with its large datasets, is a natural place to use big data solutions. However it must be stressed, that significant number of applications for machine learning in insurance industry, like ... |
Title: DT2CAM: A Decision Tree to Content Addressable Memory Framework Abstract: Decision trees are considered one of the most powerful tools for data classification. Accelerating the decision tree search is crucial for on-the-edge applications that have limited power and latency budget. In this paper, we propose a Con... |
Title: Baseline Computation for Attribution Methods Based on Interpolated Inputs Abstract: We discuss a way to find a well behaved baseline for attribution methods that work by feeding a neural network with a sequence of interpolated inputs between two given inputs. Then, we test it with our novel Riemann-Stieltjes Int... |
Title: On the dynamics of credit history and social interaction features, and their impact on creditworthiness assessment performance Abstract: For more than a half-century, credit risk management has used credit scoring models in each of its well-defined stages to manage credit risk. Application scoring is used to dec... |
Title: Reinforcement learning on graphs: A survey Abstract: Graph mining tasks arise from many different application domains, ranging from social networks, transportation to E-commerce, etc., which have been receiving great attention from the theoretical and algorithmic design communities in recent years, and there has... |
Title: A quantum generative model for multi-dimensional time series using Hamiltonian learning Abstract: Synthetic data generation has proven to be a promising solution for addressing data availability issues in various domains. Even more challenging is the generation of synthetic time series data, where one has to pre... |
Title: A Unified Cascaded Encoder ASR Model for Dynamic Model Sizes Abstract: In this paper, we propose a dynamic cascaded encoder Automatic Speech Recognition (ASR) model, which unifies models for different deployment scenarios. Moreover, the model can significantly reduce model size and power consumption without loss... |
Title: Context-based Deep Learning Architecture with Optimal Integration Layer for Image Parsing Abstract: Deep learning models have been efficient lately on image parsing tasks. However, deep learning models are not fully capable of exploiting visual and contextual information simultaneously. The proposed three-layer ... |
Title: Research on Intellectual Property Resource Profile and Evolution Law Abstract: In the era of big data, intellectual property-oriented scientific and technological resources show the trend of large data scale, high information density and low value density, which brings severe challenges to the effective use of i... |
Title: Approximation of Lipschitz Functions using Deep Spline Neural Networks Abstract: Lipschitz-constrained neural networks have many applications in machine learning. Since designing and training expressive Lipschitz-constrained networks is very challenging, there is a need for improved methods and a better theoreti... |
Title: CowClip: Reducing CTR Prediction Model Training Time from 12 hours to 10 minutes on 1 GPU Abstract: The click-through rate (CTR) prediction task is to predict whether a user will click on the recommended item. As mind-boggling amounts of data are produced online daily, accelerating CTR prediction model training ... |
Title: Encoding Domain Knowledge in Multi-view Latent Variable Models: A Bayesian Approach with Structured Sparsity Abstract: Many real-world systems are described not only by data from a single source but via multiple data views. For example, in genomic medicine, a patient can be described by data from different molec... |
Title: Large-scale multi-objective influence maximisation with network downscaling Abstract: Finding the most influential nodes in a network is a computationally hard problem with several possible applications in various kinds of network-based problems. While several methods have been proposed for tackling the influenc... |
Title: Experimental Standards for Deep Learning Research: A Natural Language Processing Perspective Abstract: The field of Deep Learning (DL) has undergone explosive growth during the last decade, with a substantial impact on Natural Language Processing (NLP) as well. Yet, as with other fields employing DL techniques, ... |
Title: Deep Learning for Effective and Efficient Reduction of Large Adaptation Spaces in Self-Adaptive Systems Abstract: Many software systems today face uncertain operating conditions, such as sudden changes in the availability of resources or unexpected user behavior. Without proper mitigation these uncertainties can... |
Title: Neural Operator with Regularity Structure for Modeling Dynamics Driven by SPDEs Abstract: Stochastic partial differential equations (SPDEs) are significant tools for modeling dynamics in many areas including atmospheric sciences and physics. Neural Operators, generations of neural networks with capability of lea... |
Title: Generalization Error Bounds for Multiclass Sparse Linear Classifiers Abstract: We consider high-dimensional multiclass classification by sparse multinomial logistic regression. Unlike binary classification, in the multiclass setup one can think about an entire spectrum of possible notions of sparsity associated ... |
Title: Overparameterized Linear Regression under Adversarial Attacks Abstract: As machine learning models start to be used in critical applications, their vulnerabilities and brittleness become a pressing concern. Adversarial attacks are a popular framework for studying these vulnerabilities. In this work, we study the... |
Title: Enabling Synthetic Data adoption in regulated domains Abstract: The switch from a Model-Centric to a Data-Centric mindset is putting emphasis on data and its quality rather than algorithms, bringing forward new challenges. In particular, the sensitive nature of the information in highly regulated scenarios needs... |
Title: Active Diffusion and VCA-Assisted Image Segmentation of Hyperspectral Images Abstract: Hyperspectral images encode rich structure that can be exploited for material discrimination by machine learning algorithms. This article introduces the Active Diffusion and VCA-Assisted Image Segmentation (ADVIS) for active m... |
Title: Estimation of stellar atmospheric parameters from LAMOST DR8 low-resolution spectra with 20$\leq$SNR$<$30 Abstract: The accuracy of the estimated stellar atmospheric parameter decreases evidently with the decreasing of spectral signal-to-noise ratio (SNR) and there are a huge amount of this kind observations, es... |
Title: Automatic Multi-Label Prompting: Simple and Interpretable Few-Shot Classification Abstract: Prompt-based learning (i.e., prompting) is an emerging paradigm for exploiting knowledge learned by a pretrained language model. In this paper, we propose Automatic Multi-Label Prompting (AMuLaP), a simple yet effective m... |
Title: Deep Learning-based Framework for Automatic Cranial Defect Reconstruction and Implant Modeling Abstract: The goal of this work is to propose a robust, fast, and fully automatic method for personalized cranial defect reconstruction and implant modeling. We propose a two-step deep learning-based method using a mod... |
Title: Production federated keyword spotting via distillation, filtering, and joint federated-centralized training Abstract: We trained a keyword spotting model using federated learning on real user devices and observed significant improvements when the model was deployed for inference on phones. To compensate for data... |
Title: Distributionally Robust Models with Parametric Likelihood Ratios Abstract: As machine learning models are deployed ever more broadly, it becomes increasingly important that they are not only able to perform well on their training distribution, but also yield accurate predictions when confronted with distribution... |
Title: Meaningful machine learning models and machine-learned pharmacophores from fragment screening campaigns Abstract: Machine learning (ML) is widely used in drug discovery to train models that predict protein-ligand binding. These models are of great value to medicinal chemists, in particular if they provide case-s... |
Title: LDPC codes: comparing cluster graphs to factor graphs Abstract: We present a comparison study between a cluster and factor graph representation of LDPC codes. In probabilistic graphical models, cluster graphs retain useful dependence between random variables during inference, which are advantageous in terms of c... |
Title: AHP: Learning to Negative Sample for Hyperedge Prediction Abstract: Hypergraphs (i.e., sets of hyperedges) naturally represent group relations (e.g., researchers co-authoring a paper and ingredients used together in a recipe), each of which corresponds to a hyperedge (i.e., a subset of nodes). Predicting future ... |
Title: A Review of Machine Learning Methods Applied to Structural Dynamics and Vibroacoustic Abstract: The use of Machine Learning (ML) has rapidly spread across several fields, having encountered many applications in Structural Dynamics and Vibroacoustic (SD\&V). The increasing capabilities of ML to unveil insights fr... |
Title: Online greedy identification of linear dynamical systems Abstract: This work addresses the problem of exploration in an unknown environment. For linear dynamical systems, we use an experimental design framework and introduce an online greedy policy where the control maximizes the information of the next step. In... |
Title: Coverage and Capacity Optimization in STAR-RISs Assisted Networks: A Machine Learning Approach Abstract: Coverage and capacity are the important metrics for performance evaluation in wireless networks, while the coverage and capacity have several conflicting relationships, e.g. high transmit power contributes to... |
Title: Receding Neuron Importances for Structured Pruning Abstract: Structured pruning efficiently compresses networks by identifying and removing unimportant neurons. While this can be elegantly achieved by applying sparsity-inducing regularisation on BatchNorm parameters, an L1 penalty would shrink all scaling factor... |
Title: Flexible Multiple-Objective Reinforcement Learning for Chip Placement Abstract: Recently, successful applications of reinforcement learning to chip placement have emerged. Pretrained models are necessary to improve efficiency and effectiveness. Currently, the weights of objective metrics (e.g., wirelength, conge... |
Title: Aspirations and Practice of Model Documentation: Moving the Needle with Nudging and Traceability Abstract: Machine learning models have been widely developed, released, and adopted in numerous applications. Meanwhile, the documentation practice for machine learning models often falls short of established practic... |
Title: Label Augmentation with Reinforced Labeling for Weak Supervision Abstract: Weak supervision (WS) is an alternative to the traditional supervised learning to address the need for ground truth. Data programming is a practical WS approach that allows programmatic labeling data samples using labeling functions (LFs)... |
Title: Receptive Field Analysis of Temporal Convolutional Networks for Monaural Speech Dereverberation Abstract: Speech dereverberation is often an important requirement in robust speech processing tasks. Supervised deep learning (DL) models give state-of-the-art performance for single-channel speech dereverberation. T... |
Title: Random Graph Embedding and Joint Sparse Regularization for Multi-label Feature Selection Abstract: Multi-label learning is often used to mine the correlation between variables and multiple labels, and its research focuses on fully extracting the information between variables and labels. The $\ell_{2,1}$ regulari... |
Title: Is Speech Pathology a Biomarker in Automatic Speaker Verification? Abstract: With the advancements in deep learning (DL) and an increasing interest in data-driven speech processing methods, a major challenge for speech data scientists in the healthcare domain is the anonymization of pathological speech, which is... |
Title: Hybrid Neural Network Augmented Physics-based Models for Nonlinear Filtering Abstract: In this paper we present a hybrid neural network augmented physics-based modeling (APBM) framework for Bayesian nonlinear latent space estimation. The proposed APBM strategy allows for model adaptation when new operation condi... |
Title: Data-heterogeneity-aware Mixing for Decentralized Learning Abstract: Decentralized learning provides an effective framework to train machine learning models with data distributed over arbitrary communication graphs. However, most existing approaches toward decentralized learning disregard the interaction between... |
Title: Clinical trial site matching with improved diversity using fair policy learning Abstract: The ongoing pandemic has highlighted the importance of reliable and efficient clinical trials in healthcare. Trial sites, where the trials are conducted, are chosen mainly based on feasibility in terms of medical expertise ... |
Title: DL4SciVis: A State-of-the-Art Survey on Deep Learning for Scientific Visualization Abstract: Since 2016, we have witnessed the tremendous growth of artificial intelligence+visualization (AI+VIS) research. However, existing survey papers on AI+VIS focus on visual analytics and information visualization, not scien... |
Title: Out-of-distribution Detection with Deep Nearest Neighbors Abstract: Out-of-distribution (OOD) detection is a critical task for deploying machine learning models in the open world. Distance-based methods have demonstrated promise, where testing samples are detected as OOD if they are relatively far away from in-d... |
Title: FactGraph: Evaluating Factuality in Summarization with Semantic Graph Representations Abstract: Despite recent improvements in abstractive summarization, most current approaches generate summaries that are not factually consistent with the source document, severely restricting their trust and usage in real-world... |
Title: Safer Autonomous Driving in a Stochastic, Partially-Observable Environment by Hierarchical Contingency Planning Abstract: When learning to act in a stochastic, partially observable environment, an intelligent agent should be prepared to anticipate a change in its belief of the environment state, and be capable o... |
Title: Scalable Training of Language Models using JAX pjit and TPUv4 Abstract: Modern large language models require distributed training strategies due to their size. The challenges of efficiently and robustly training them are met with rapid developments on both software and hardware frontiers. In this technical repor... |
Title: Sentiment Analysis of Political Tweets for Israel using Machine Learning Abstract: Sentiment Analysis is a vital research topic in the field of Computer Science. With the accelerated development of Information Technology and social networks, a massive amount of data related to comment texts has been generated on... |
Title: Decentralized Collaborative Learning Framework for Next POI Recommendation Abstract: Next Point-of-Interest (POI) recommendation has become an indispensable functionality in Location-based Social Networks (LBSNs) due to its effectiveness in helping people decide the next POI to visit. However, accurate recommend... |
Title: Learning Self-Modulating Attention in Continuous Time Space with Applications to Sequential Recommendation Abstract: User interests are usually dynamic in the real world, which poses both theoretical and practical challenges for learning accurate preferences from rich behavior data. Among existing user behavior ... |
Title: A pipeline and comparative study of 12 machine learning models for text classification Abstract: Text-based communication is highly favoured as a communication method, especially in business environments. As a result, it is often abused by sending malicious messages, e.g., spam emails, to deceive users into rela... |
Title: Bayesian Negative Sampling for Recommendation Abstract: How to sample high quality negative instances from unlabeled data, i.e., negative sampling, is important for training implicit collaborative filtering and contrastive learning models. Although previous studies have proposed some approaches to sample informa... |
Title: Estimating Structural Disparities for Face Models Abstract: In machine learning, disparity metrics are often defined by measuring the difference in the performance or outcome of a model, across different sub-populations (groups) of datapoints. Thus, the inputs to disparity quantification consist of a model's pre... |
Title: Character-focused Video Thumbnail Retrieval Abstract: We explore retrieving character-focused video frames as candidates for being video thumbnails. To evaluate each frame of the video based on the character(s) present in it, characters (faces) are evaluated in two aspects: Facial-expression: We train a CNN mode... |
Title: A Study of Causal Confusion in Preference-Based Reward Learning Abstract: Learning robot policies via preference-based reward learning is an increasingly popular method for customizing robot behavior. However, in recent years, there has been a growing body of anecdotal evidence that learning reward functions fro... |
Title: Modularity benefits reinforcement learning agents with competing homeostatic drives Abstract: The problem of balancing conflicting needs is fundamental to intelligence. Standard reinforcement learning algorithms maximize a scalar reward, which requires combining different objective-specific rewards into a single... |
Title: Formal Language Recognition by Hard Attention Transformers: Perspectives from Circuit Complexity Abstract: This paper analyzes three formal models of Transformer encoders that differ in the form of their self-attention mechanism: unique hard attention (UHAT); generalized unique hard attention (GUHAT), which gene... |
Title: A Natural Language Processing Approach for Instruction Set Architecture Identification Abstract: Binary analysis of software is a critical step in cyber forensics applications such as program vulnerability assessment and malware detection. This involves interpreting instructions executed by software and often ne... |
Title: CAMERO: Consistency Regularized Ensemble of Perturbed Language Models with Weight Sharing Abstract: Model ensemble is a popular approach to produce a low-variance and well-generalized model. However, it induces large memory and inference costs, which are often not affordable for real-world deployment. Existing w... |
Title: Performance Assessment of different Machine Learning Algorithm for Life-Time Prediction of Solder Joints based on Synthetic Data Abstract: This paper proposes a computationally efficient methodology to predict the damage progression in solder contacts of electronic components using temperature-time curves. For t... |
Title: Clifford Circuits can be Properly PAC Learned if and only if $\textsf{RP}=\textsf{NP}$ Abstract: Given a dataset of input states, measurements, and probabilities, is it possible to efficiently predict the measurement probabilities associated with a quantum circuit? Recent work of Caro and Datta (2020) studied th... |
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