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Title: Entropic Associative Memory for Manuscript Symbols Abstract: Manuscript symbols can be stored, recognized and retrieved from an entropic digital memory that is associative and distributed but yet declarative; memory retrieval is a constructive operation, memory cues to objects not contained in the memory are rej... |
Title: Retrieval-Augmented Reinforcement Learning Abstract: Most deep reinforcement learning (RL) algorithms distill experience into parametric behavior policies or value functions via gradient updates. While effective, this approach has several disadvantages: (1) it is computationally expensive, (2) it can take many u... |
Title: Time-Correlated Sparsification for Efficient Over-the-Air Model Aggregation in Wireless Federated Learning Abstract: Federated edge learning (FEEL) is a promising distributed machine learning (ML) framework to drive edge intelligence applications. However, due to the dynamic wireless environments and the resourc... |
Title: Synthetic Control As Online Linear Regression Abstract: This paper notes a simple connection between synthetic control and online learning. Specifically, we recognize synthetic control as an instance of Follow-The-Leader (FTL). Standard results in online convex optimization then imply that, even when outcomes ar... |
Title: ADD 2022: the First Audio Deep Synthesis Detection Challenge Abstract: Audio deepfake detection is an emerging topic, which was included in the ASVspoof 2021. However, the recent shared tasks have not covered many real-life and challenging scenarios. The first Audio Deep synthesis Detection challenge (ADD) was m... |
Title: A Survey of Explainable Reinforcement Learning Abstract: Explainable reinforcement learning (XRL) is an emerging subfield of explainable machine learning that has attracted considerable attention in recent years. The goal of XRL is to elucidate the decision-making process of learning agents in sequential decisio... |
Title: A Survey on Deep Reinforcement Learning-based Approaches for Adaptation and Generalization Abstract: Deep Reinforcement Learning (DRL) aims to create intelligent agents that can learn to solve complex problems efficiently in a real-world environment. Typically, two learning goals: adaptation and generalization a... |
Title: Design-Bench: Benchmarks for Data-Driven Offline Model-Based Optimization Abstract: Black-box model-based optimization (MBO) problems, where the goal is to find a design input that maximizes an unknown objective function, are ubiquitous in a wide range of domains, such as the design of proteins, DNA sequences, a... |
Title: Transformer for Graphs: An Overview from Architecture Perspective Abstract: Recently, Transformer model, which has achieved great success in many artificial intelligence fields, has demonstrated its great potential in modeling graph-structured data. Till now, a great variety of Transformers has been proposed to ... |
Title: MLP-ASR: Sequence-length agnostic all-MLP architectures for speech recognition Abstract: We propose multi-layer perceptron (MLP)-based architectures suitable for variable length input. MLP-based architectures, recently proposed for image classification, can only be used for inputs of a fixed, pre-defined size. H... |
Title: End-to-End Training of Both Translation Models in the Back-Translation Framework Abstract: Semi-supervised learning algorithms in neural machine translation (NMT) have significantly improved translation quality compared to the supervised learning algorithms by using additional monolingual corpora. Among them, ba... |
Title: Full-Span Log-Linear Model and Fast Learning Algorithm Abstract: The full-span log-linear(FSLL) model introduced in this paper is considered an $n$-th order Boltzmann machine, where $n$ is the number of all variables in the target system. Let $X=(X_0,...,X_{n-1})$ be finite discrete random variables that can tak... |
Title: Structural and Semantic Contrastive Learning for Unsupervised Node Representation Learning Abstract: Graph Contrastive Learning (GCL) recently has drawn much research interest for learning generalizable node representations in a self-supervised manner. In general, the contrastive learning process in GCL is perfo... |
Title: Multi-Objective Model Selection for Time Series Forecasting Abstract: Research on time series forecasting has predominantly focused on developing methods that improve accuracy. However, other criteria such as training time or latency are critical in many real-world applications. We therefore address the question... |
Title: Dynamic Object Comprehension: A Framework For Evaluating Artificial Visual Perception Abstract: Augmented and Mixed Reality are emerging as likely successors to the mobile internet. However, many technical challenges remain. One of the key requirements of these systems is the ability to create a continuity betwe... |
Title: Learning continuous models for continuous physics Abstract: Dynamical systems that evolve continuously over time are ubiquitous throughout science and engineering. Machine learning (ML) provides data-driven approaches to model and predict the dynamics of such systems. A core issue with this approach is that ML m... |
Title: A Study of Designing Compact Audio-Visual Wake Word Spotting System Based on Iterative Fine-Tuning in Neural Network Pruning Abstract: Audio-only-based wake word spotting (WWS) is challenging under noisy conditions due to environmental interference in signal transmission. In this paper, we investigate on designi... |
Title: A hybrid 2-stage vision transformer for artificial intelligence-assisted 5 class pathologic diagnosis of gastric endoscopic biopsies: a diagnostic tool for guiding gastric cancer treatment Abstract: Gastric endoscopic screening is an effective way to decide appropriate gastric cancer (GC) treatment at an early s... |
Title: Survey on Self-supervised Representation Learning Using Image Transformations Abstract: Deep neural networks need huge amount of training data, while in real world there is a scarcity of data available for training purposes. To resolve these issues, self-supervised learning (SSL) methods are used. SSL using geom... |
Title: SAITS: Self-Attention-based Imputation for Time Series Abstract: Missing data in time series is a pervasive problem that puts obstacles in the way of advanced analysis. A popular solution is imputation, where the fundamental challenge is to determine what values should be filled in. This paper proposes SAITS, a ... |
Title: DeepHybrid: Deep Learning on Automotive Radar Spectra and Reflections for Object Classification Abstract: Automated vehicles need to detect and classify objects and traffic participants accurately. Reliable object classification using automotive radar sensors has proved to be challenging. We propose a method tha... |
Title: End-to-end Music Remastering System Using Self-supervised and Adversarial Training Abstract: Mastering is an essential step in music production, but it is also a challenging task that has to go through the hands of experienced audio engineers, where they adjust tone, space, and volume of a song. Remastering foll... |
Title: Recovering Unbalanced Communities in the Stochastic Block Model With Application to Clustering with a Faulty Oracle Abstract: The stochastic block model (SBM) is a fundamental model for studying graph clustering or community detection in networks. It has received great attention in the last decade and the balanc... |
Title: Contrastive Meta Learning with Behavior Multiplicity for Recommendation Abstract: A well-informed recommendation framework could not only help users identify their interested items, but also benefit the revenue of various online platforms (e.g., e-commerce, social media). Traditional recommendation models usuall... |
Title: A Collection and Categorization of Open-Source Wind and Wind Power Datasets Abstract: Wind power and other forms of renewable energy sources play an ever more important role in the energy supply of today's power grids. Forecasting renewable energy sources has therefore become essential in balancing the power gri... |
Title: Point Cloud Generation with Continuous Conditioning Abstract: Generative models can be used to synthesize 3D objects of high quality and diversity. However, there is typically no control over the properties of the generated object.This paper proposes a novel generative adversarial network (GAN) setup that genera... |
Title: Mitigating Closed-model Adversarial Examples with Bayesian Neural Modeling for Enhanced End-to-End Speech Recognition Abstract: In this work, we aim to enhance the system robustness of end-to-end automatic speech recognition (ASR) against adversarially-noisy speech examples. We focus on a rigorous and empirical ... |
Title: Does the End Justify the Means? On the Moral Justification of Fairness-Aware Machine Learning Abstract: Despite an abundance of fairness-aware machine learning (fair-ml) algorithms, the moral justification of how these algorithms enforce fairness metrics is largely unexplored. The goal of this paper is to elicit... |
Title: When, where, and how to add new neurons to ANNs Abstract: Neurogenesis in ANNs is an understudied and difficult problem, even compared to other forms of structural learning like pruning. By decomposing it into triggers and initializations, we introduce a framework for studying the various facets of neurogenesis:... |
Title: Information Theory with Kernel Methods Abstract: We consider the analysis of probability distributions through their associated covariance operators from reproducing kernel Hilbert spaces. We show that the von Neumann entropy and relative entropy of these operators are intimately related to the usual notions of ... |
Title: Oracle-Efficient Online Learning for Beyond Worst-Case Adversaries Abstract: In this paper, we study oracle-efficient algorithms for beyond worst-case analysis of online learning. We focus on two settings. First, the smoothed analysis setting of [RST11, HRS21] where an adversary is constrained to generating samp... |
Title: Delay-adaptive step-sizes for asynchronous learning Abstract: In scalable machine learning systems, model training is often parallelized over multiple nodes that run without tight synchronization. Most analysis results for the related asynchronous algorithms use an upper bound on the information delays in the sy... |
Title: Efficient and Reliable Probabilistic Interactive Learning with Structured Outputs Abstract: In this position paper, we study interactive learning for structured output spaces, with a focus on active learning, in which labels are unknown and must be acquired, and on skeptical learning, in which the labels are noi... |
Title: Robust SVM Optimization in Banach spaces Abstract: We address the issue of binary classification in Banach spaces in presence of uncertainty. We show that a number of results from classical support vector machines theory can be appropriately generalised to their robust counterpart in Banach spaces. These include... |
Title: An Equivalence Between Data Poisoning and Byzantine Gradient Attacks Abstract: To study the resilience of distributed learning, the "Byzantine" literature considers a strong threat model where workers can report arbitrary gradients to the parameter server. Whereas this model helped obtain several fundamental res... |
Title: Gradients without Backpropagation Abstract: Using backpropagation to compute gradients of objective functions for optimization has remained a mainstay of machine learning. Backpropagation, or reverse-mode differentiation, is a special case within the general family of automatic differentiation algorithms that al... |
Title: CoFED: Cross-Silo Heterogeneous Federated Multitask Learning via Cotraining Abstract: Federated learning (FL) is a machine learning technique that enables participants to collaboratively train high-quality models without exchanging their private data. Participants utilizing cross-silo FL (CS-FL) settings are ind... |
Title: On the evaluation of (meta-)solver approaches Abstract: Meta-solver approaches exploits a number of individual solvers to potentially build a better solver. To assess the performance of meta-solvers, one can simply adopt the metrics typically used for individual solvers (e.g., runtime or solution quality), or em... |
Title: Revisiting Over-smoothing in BERT from the Perspective of Graph Abstract: Recently over-smoothing phenomenon of Transformer-based models is observed in both vision and language fields. However, no existing work has delved deeper to further investigate the main cause of this phenomenon. In this work, we make the ... |
Title: The merged-staircase property: a necessary and nearly sufficient condition for SGD learning of sparse functions on two-layer neural networks Abstract: It is currently known how to characterize functions that neural networks can learn with SGD for two extremal parameterizations: neural networks in the linear regi... |
Title: Measuring Trustworthiness or Automating Physiognomy? A Comment on Safra, Chevallier, Gr\`ezes, and Baumard (2020) Abstract: Interpersonal trust - a shared display of confidence and vulnerability toward other individuals - can be seen as instrumental in the development of human societies. Safra, Chevallier, Gr\`e... |
Title: Winograd Convolution: A Perspective from Fault Tolerance Abstract: Winograd convolution is originally proposed to reduce the computing overhead by converting multiplication in neural network (NN) with addition via linear transformation. Other than the computing efficiency, we observe its great potential in impro... |
Title: Where Is My Training Bottleneck? Hidden Trade-Offs in Deep Learning Preprocessing Pipelines Abstract: Preprocessing pipelines in deep learning aim to provide sufficient data throughput to keep the training processes busy. Maximizing resource utilization is becoming more challenging as the throughput of training ... |
Title: Detecting and Learning the Unknown in Semantic Segmentation Abstract: Semantic segmentation is a crucial component for perception in automated driving. Deep neural networks (DNNs) are commonly used for this task and they are usually trained on a closed set of object classes appearing in a closed operational doma... |
Title: Learning stochastic dynamics and predicting emergent behavior using transformers Abstract: We show that a neural network originally designed for language processing can learn the dynamical rules of a stochastic system by observation of a single dynamical trajectory of the system, and can accurately predict its e... |
Title: Improving Rating and Relevance with Point-of-Interest Recommender System Abstract: The recommendation of points of interest (POIs) is essential in location-based social networks. It makes it easier for users and locations to share information. Recently, researchers tend to recommend POIs by treating them as larg... |
Title: Ensemble Conformalized Quantile Regression for Probabilistic Time Series Forecasting Abstract: This paper presents a novel probabilistic forecasting method called ensemble conformalized quantile regression (EnCQR). EnCQR constructs distribution-free and approximately marginally valid prediction intervals (PIs), ... |
Title: Global Convergence of Sub-gradient Method for Robust Matrix Recovery: Small Initialization, Noisy Measurements, and Over-parameterization Abstract: In this work, we study the performance of sub-gradient method (SubGM) on a natural nonconvex and nonsmooth formulation of low-rank matrix recovery with $\ell_1$-loss... |
Title: Hamilton-Jacobi equations on graphs with applications to semi-supervised learning and data depth Abstract: Shortest path graph distances are widely used in data science and machine learning, since they can approximate the underlying geodesic distance on the data manifold. However, the shortest path distance is h... |
Title: Grammar-Based Grounded Lexicon Learning Abstract: We present Grammar-Based Grounded Lexicon Learning (G2L2), a lexicalist approach toward learning a compositional and grounded meaning representation of language from grounded data, such as paired images and texts. At the core of G2L2 is a collection of lexicon en... |
Title: Should I send this notification? Optimizing push notifications decision making by modeling the future Abstract: Most recommender systems are myopic, that is they optimize based on the immediate response of the user. This may be misaligned with the true objective, such as creating long term user satisfaction. In ... |
Title: GRAPHSHAP: Motif-based Explanations for Black-box Graph Classifiers Abstract: Most methods for explaining black-box classifiers (e.g., on tabular data, images, or time series) rely on measuring the impact that the removal/perturbation of features has on the model output. This forces the explanation language to m... |
Title: Learning and Evaluating Graph Neural Network Explanations based on Counterfactual and Factual Reasoning Abstract: Structural data well exists in Web applications, such as social networks in social media, citation networks in academic websites, and threads data in online forums. Due to the complex topology, it is... |
Title: Human-Algorithm Collaboration: Achieving Complementarity and Avoiding Unfairness Abstract: Much of machine learning research focuses on predictive accuracy: given a task, create a machine learning model (or algorithm) that maximizes accuracy. In many settings, however, the final prediction or decision of a syste... |
Title: Multi-stage Ensemble Model for Cross-market Recommendation Abstract: This paper describes the solution of our team PolimiRank for the WSDM Cup 2022 on cross-market recommendation. The goal of the competition is to effectively exploit the information extracted from different markets to improve the ranking accurac... |
Title: LAMP: Extracting Text from Gradients with Language Model Priors Abstract: Recent work shows that sensitive user data can be reconstructed from gradient updates, breaking the key privacy promise of federated learning. While success was demonstrated primarily on image data, these methods do not directly transfer t... |
Title: Universality of empirical risk minimization Abstract: Consider supervised learning from i.i.d. samples $\{{\boldsymbol x}_i,y_i\}_{i\le n}$ where ${\boldsymbol x}_i \in\mathbb{R}^p$ are feature vectors and ${y} \in \mathbb{R}$ are labels. We study empirical risk minimization over a class of functions that are pa... |
Title: The Exact Class of Graph Functions Generated by Graph Neural Networks Abstract: Given a graph function, defined on an arbitrary set of edge weights and node features, does there exist a Graph Neural Network (GNN) whose output is identical to the graph function? In this paper, we fully answer this question and ch... |
Title: General Cyclical Training of Neural Networks Abstract: This paper describes the principle of "General Cyclical Training" in machine learning, where training starts and ends with "easy training" and the "hard training" happens during the middle epochs. We propose several manifestations for training neural network... |
Title: Data-SUITE: Data-centric identification of in-distribution incongruous examples Abstract: Systematic quantification of data quality is critical for consistent model performance. Prior works have focused on out-of-distribution data. Instead, we tackle an understudied yet equally important problem of characterizin... |
Title: Adiabatic Quantum Computing for Multi Object Tracking Abstract: Multi-Object Tracking (MOT) is most often approached in the tracking-by-detection paradigm, where object detections are associated through time. The association step naturally leads to discrete optimization problems. As these optimization problems a... |
Title: Scalable approach to many-body localization via quantum data Abstract: We are interested in how quantum data can allow for practical solutions to otherwise difficult computational problems. A notoriously difficult phenomenon from quantum many-body physics is the emergence of many-body localization (MBL). So far,... |
Title: RemixIT: Continual self-training of speech enhancement models via bootstrapped remixing Abstract: We present RemixIT, a simple yet effective self-supervised method for training speech enhancement without the need of a single isolated in-domain speech nor a noise waveform. Our approach overcomes limitations of pr... |
Title: Fast online inference for nonlinear contextual bandit based on Generative Adversarial Network Abstract: This work addresses the efficiency concern on inferring a nonlinear contextual bandit when the number of arms $n$ is very large. We propose a neural bandit model with an end-to-end training process to efficien... |
Title: Graph Data Augmentation for Graph Machine Learning: A Survey Abstract: Data augmentation has recently seen increased interest in graph machine learning given its ability of creating extra training data and improving model generalization. Despite this recent upsurge, this area is still relatively underexplored, d... |
Title: An alternative approach to train neural networks using monotone variational inequality Abstract: Despite the vast empirical success of neural networks, theoretical understanding of the training procedures remains limited, especially in providing performance guarantees of testing performance due to the non-convex... |
Title: Improving English to Sinhala Neural Machine Translation using Part-of-Speech Tag Abstract: The performance of Neural Machine Translation (NMT) depends significantly on the size of the available parallel corpus. Due to this fact, low resource language pairs demonstrate low translation performance compared to high... |
Title: Curriculum optimization for low-resource speech recognition Abstract: Modern end-to-end speech recognition models show astonishing results in transcribing audio signals into written text. However, conventional data feeding pipelines may be sub-optimal for low-resource speech recognition, which still remains a ch... |
Title: BADDr: Bayes-Adaptive Deep Dropout RL for POMDPs Abstract: While reinforcement learning (RL) has made great advances in scalability, exploration and partial observability are still active research topics. In contrast, Bayesian RL (BRL) provides a principled answer to both state estimation and the exploration-exp... |
Title: Word Embeddings for Automatic Equalization in Audio Mixing Abstract: In recent years, machine learning has been widely adopted to automate the audio mixing process. Automatic mixing systems have been applied to various audio effects such as gain-adjustment, stereo panning, equalization, and reverberation. These ... |
Title: ST-MoE: Designing Stable and Transferable Sparse Expert Models Abstract: Scale has opened new frontiers in natural language processing -- but at a high cost. In response, Mixture-of-Experts (MoE) and Switch Transformers have been proposed as an energy efficient path to even larger and more capable language model... |
Title: Sampling Approximately Low-Rank Ising Models: MCMC meets Variational Methods Abstract: We consider Ising models on the hypercube with a general interaction matrix $J$, and give a polynomial time sampling algorithm when all but $O(1)$ eigenvalues of $J$ lie in an interval of length one, a situation which occurs i... |
Title: Combining Varied Learners for Binary Classification using Stacked Generalization Abstract: The Machine Learning has various learning algorithms that are better in some or the other aspect when compared with each other but a common error that all algorithms will suffer from is training data with very high dimensi... |
Title: Machine learning models and facial regions videos for estimating heart rate: a review on Patents, Datasets and Literature Abstract: Estimating heart rate is important for monitoring users in various situations. Estimates based on facial videos are increasingly being researched because it makes it possible to mon... |
Title: Graph Convolutional Networks for Multi-modality Medical Imaging: Methods, Architectures, and Clinical Applications Abstract: Image-based characterization and disease understanding involve integrative analysis of morphological, spatial, and topological information across biological scales. The development of grap... |
Title: FLAME: Federated Learning Across Multi-device Environments Abstract: Federated Learning (FL) enables distributed training of machine learning models while keeping personal data on user devices private. While we witness increasing applications of FL in the area of mobile sensing, such as human-activity recognitio... |
Title: A Distributed Algorithm for Measure-valued Optimization with Additive Objective Abstract: We propose a distributed nonparametric algorithm for solving measure-valued optimization problems with additive objectives. Such problems arise in several contexts in stochastic learning and control including Langevin sampl... |
Title: Handling Imbalanced Datasets Through Optimum-Path Forest Abstract: In the last decade, machine learning-based approaches became capable of performing a wide range of complex tasks sometimes better than humans, demanding a fraction of the time. Such an advance is partially due to the exponential growth in the amo... |
Title: When, Why, and Which Pretrained GANs Are Useful? Abstract: The literature has proposed several methods to finetune pretrained GANs on new datasets, which typically results in higher performance compared to training from scratch, especially in the limited-data regime. However, despite the apparent empirical benef... |
Title: Improving Intrinsic Exploration with Language Abstractions Abstract: Reinforcement learning (RL) agents are particularly hard to train when rewards are sparse. One common solution is to use intrinsic rewards to encourage agents to explore their environment. However, recent intrinsic exploration methods often use... |
Title: Enhanced DeepONet for Modeling Partial Differential Operators Considering Multiple Input Functions Abstract: Machine learning, especially deep learning is gaining much attention due to the breakthrough performance in various cognitive applications. Recently, neural networks (NN) have been intensively explored to... |
Title: Rethinking Machine Learning Robustness via its Link with the Out-of-Distribution Problem Abstract: Despite multiple efforts made towards robust machine learning (ML) models, their vulnerability to adversarial examples remains a challenging problem that calls for rethinking the defense strategy. In this paper, we... |
Title: Symphony: Composing Interactive Interfaces for Machine Learning Abstract: Interfaces for machine learning (ML), information and visualizations about models or data, can help practitioners build robust and responsible ML systems. Despite their benefits, recent studies of ML teams and our interviews with practitio... |
Title: GNN-Surrogate: A Hierarchical and Adaptive Graph Neural Network for Parameter Space Exploration of Unstructured-Mesh Ocean Simulations Abstract: We propose GNN-Surrogate, a graph neural network-based surrogate model to explore the parameter space of ocean climate simulations. Parameter space exploration is impor... |
Title: Deep Interest Highlight Network for Click-Through Rate Prediction in Trigger-Induced Recommendation Abstract: In many classical e-commerce platforms, personalized recommendation has been proven to be of great business value, which can improve user satisfaction and increase the revenue of platforms. In this paper... |
Title: Toward a traceable, explainable, and fairJD/Resume recommendation system Abstract: In the last few decades, companies are interested to adopt an online automated recruitment process in an international recruitment environment. The problem is that the recruitment of employees through the manual procedure is a tim... |
Title: Understanding and Shifting Preferences for Battery Electric Vehicles Abstract: Identifying personalized interventions for an individual is an important task. Recent work has shown that interventions that do not consider the demographic background of individual consumers can, in fact, produce the reverse effect, ... |
Title: Simulating User-Level Twitter Activity with XGBoost and Probabilistic Hybrid Models Abstract: The Volume-Audience-Match simulator, or VAM was applied to predict future activity on Twitter related to international economic affairs. VAM was applied to do timeseries forecasting to predict the: (1) number of total a... |
Title: High-performance automatic categorization and attribution of inventory catalogs Abstract: Techniques of machine learning for automatic text categorization are applied and adapted for the problem of inventory catalog data attribution, with different approaches explored and optimal solution addressing the tradeoff... |
Title: Ensemble and Multimodal Approach for Forecasting Cryptocurrency Price Abstract: Since the birth of Bitcoin in 2009, cryptocurrencies have emerged to become a global phenomenon and an important decentralized financial asset. Due to this decentralization, the value of these digital currencies against fiat currenci... |
Title: Stock Embeddings: Learning Distributed Representations for Financial Assets Abstract: Identifying meaningful relationships between the price movements of financial assets is a challenging but important problem in a variety of financial applications. However with recent research, particularly those using machine ... |
Title: Private Quantiles Estimation in the Presence of Atoms Abstract: We address the differentially private estimation of multiple quantiles (MQ) of a dataset, a key building block in modern data analysis. We apply the recent non-smoothed Inverse Sensitivity (IS) mechanism to this specific problem and establish that t... |
Title: Deep Reinforcement Learning Based Multi-Access Edge Computing Schedule for Internet of Vehicle Abstract: As intelligent transportation systems been implemented broadly and unmanned arial vehicles (UAVs) can assist terrestrial base stations acting as multi-access edge computing (MEC) to provide a better wireless ... |
Title: Energy-Efficient Parking Analytics System using Deep Reinforcement Learning Abstract: Advances in deep vision techniques and ubiquity of smart cameras will drive the next generation of video analytics. However, video analytics applications consume vast amounts of energy as both deep learning techniques and camer... |
Title: Multimodal Emotion Recognition using Transfer Learning from Speaker Recognition and BERT-based models Abstract: Automatic emotion recognition plays a key role in computer-human interaction as it has the potential to enrich the next-generation artificial intelligence with emotional intelligence. It finds applicat... |
Title: Probing Pretrained Models of Source Code Abstract: Deep learning models are widely used for solving challenging code processing tasks, such as code generation or code summarization. Traditionally, a specific model architecture was carefully built to solve a particular code processing task. However, recently gene... |
Title: Fairness constraint in Structural Econometrics and Application to fair estimation using Instrumental Variables Abstract: A supervised machine learning algorithm determines a model from a learning sample that will be used to predict new observations. To this end, it aggregates individual characteristics of the ob... |
Title: Cyclical Focal Loss Abstract: The cross-entropy softmax loss is the primary loss function used to train deep neural networks. On the other hand, the focal loss function has been demonstrated to provide improved performance when there is an imbalance in the number of training samples in each class, such as in lon... |
Title: The Response Shift Paradigm to Quantify Human Trust in AI Recommendations Abstract: Explainability, interpretability and how much they affect human trust in AI systems are ultimately problems of human cognition as much as machine learning, yet the effectiveness of AI recommendations and the trust afforded by end... |
Title: A Summary of the ComParE COVID-19 Challenges Abstract: The COVID-19 pandemic has caused massive humanitarian and economic damage. Teams of scientists from a broad range of disciplines have searched for methods to help governments and communities combat the disease. One avenue from the machine learning field whic... |
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