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Title: HyBNN and FedHyBNN: (Federated) Hybrid Binary Neural Networks Abstract: Binary Neural Networks (BNNs), neural networks with weights and activations constrained to -1(0) and +1, are an alternative to deep neural networks which offer faster training, lower memory consumption and lightweight models, ideal for use i... |
Title: Why GANs are overkill for NLP Abstract: This work offers a novel theoretical perspective on why, despite numerous attempts, adversarial approaches to generative modeling (e.g., GANs) have not been as popular for certain generation tasks, particularly sequential tasks such as Natural Language Generation, as they ... |
Title: Summarization as Indirect Supervision for Relation Extraction Abstract: Relation extraction (RE) models have been challenged by their reliance on training data with expensive annotations. Considering that summarization tasks aim at acquiring concise expressions of synoptical information from the longer context, ... |
Title: Concurrent Policy Blending and System Identification for Generalized Assistive Control Abstract: In this work, we address the problem of solving complex collaborative robotic tasks subject to multiple varying parameters. Our approach combines simultaneous policy blending with system identification to create gene... |
Title: Classification of Intra-Pulse Modulation of Radar Signals by Feature Fusion Based Convolutional Neural Networks Abstract: Detection and classification of radars based on pulses they transmit is an important application in electronic warfare systems. In this work, we propose a novel deep-learning based technique ... |
Title: Learning Interface Conditions in Domain Decomposition Solvers Abstract: Domain decomposition methods are widely used and effective in the approximation of solutions to partial differential equations. Yet the optimal construction of these methods requires tedious analysis and is often available only in simplified... |
Title: Capturing cross-session neural population variability through self-supervised identification of consistent neuron ensembles Abstract: Decoding stimuli or behaviour from recorded neural activity is a common approach to interrogate brain function in research, and an essential part of brain-computer and brain-machi... |
Title: Algorithms for Weak Optimal Transport with an Application to Economics Abstract: The theory of weak optimal transport (WOT), introduced by [Gozlan et al., 2017], generalizes the classic Monge-Kantorovich framework by allowing the transport cost between one point and the points it is matched with to be nonlinear.... |
Title: Deep Learning Methods for Proximal Inference via Maximum Moment Restriction Abstract: The No Unmeasured Confounding Assumption is widely used to identify causal effects in observational studies. Recent work on proximal inference has provided alternative identification results that succeed even in the presence of... |
Title: A Learning-Based Approach to Approximate Coded Computation Abstract: Lagrange coded computation (LCC) is essential to solving problems about matrix polynomials in a coded distributed fashion; nevertheless, it can only solve the problems that are representable as matrix polynomials. In this paper, we propose AICC... |
Title: MiDAS: Multi-integrated Domain Adaptive Supervision for Fake News Detection Abstract: COVID-19 related misinformation and fake news, coined an 'infodemic', has dramatically increased over the past few years. This misinformation exhibits concept drift, where the distribution of fake news changes over time, reduci... |
Title: A Novel Weighted Ensemble Learning Based Agent for the Werewolf Game Abstract: Werewolf is a popular party game throughout the world, and research on its significance has progressed in recent years. The Werewolf game is based on conversation, and in order to win, participants must use all of their cognitive abil... |
Title: Calibration Matters: Tackling Maximization Bias in Large-scale Advertising Recommendation Systems Abstract: Calibration is defined as the ratio of the average predicted click rate to the true click rate. The optimization of calibration is essential to many online advertising recommendation systems because it dir... |
Title: Estimation of Entropy in Constant Space with Improved Sample Complexity Abstract: Recent work of Acharya et al. (NeurIPS 2019) showed how to estimate the entropy of a distribution $\mathcal D$ over an alphabet of size $k$ up to $\pm\epsilon$ additive error by streaming over $(k/\epsilon^3) \cdot \text{polylog}(1... |
Title: Towards a Holistic View on Argument Quality Prediction Abstract: Argumentation is one of society's foundational pillars, and, sparked by advances in NLP and the vast availability of text data, automated mining of arguments receives increasing attention. A decisive property of arguments is their strength or quali... |
Title: Label-invariant Augmentation for Semi-Supervised Graph Classification Abstract: Recently, contrastiveness-based augmentation surges a new climax in the computer vision domain, where some operations, including rotation, crop, and flip, combined with dedicated algorithms, dramatically increase the model generaliza... |
Title: Graph Neural Networks Are More Powerful Than we Think Abstract: Graph Neural Networks (GNNs) are powerful convolutional architectures that have shown remarkable performance in various node-level and graph-level tasks. Despite their success, the common belief is that the expressive power of GNNs is limited and th... |
Title: Improving Multi-Task Generalization via Regularizing Spurious Correlation Abstract: Multi-Task Learning (MTL) is a powerful learning paradigm to improve generalization performance via knowledge sharing. However, existing studies find that MTL could sometimes hurt generalization, especially when two tasks are les... |
Title: Causal Discovery and Injection for Feed-Forward Neural Networks Abstract: Neural networks have proven to be effective at solving a wide range of problems but it is often unclear whether they learn any meaningful causal relationship: this poses a problem for the robustness of neural network models and their use f... |
Title: Residual Dynamic Mode Decomposition: Robust and verified Koopmanism Abstract: Dynamic Mode Decomposition (DMD) describes complex dynamic processes through a hierarchy of simpler coherent features. DMD is regularly used to understand the fundamental characteristics of turbulence and is closely related to Koopman ... |
Title: Understanding Gradient Descent on Edge of Stability in Deep Learning Abstract: Deep learning experiments in Cohen et al. (2021) using deterministic Gradient Descent (GD) revealed an {\em Edge of Stability (EoS)} phase when learning rate (LR) and sharpness (\emph{i.e.}, the largest eigenvalue of Hessian) no longe... |
Title: Overcoming Language Disparity in Online Content Classification with Multimodal Learning Abstract: Advances in Natural Language Processing (NLP) have revolutionized the way researchers and practitioners address crucial societal problems. Large language models are now the standard to develop state-of-the-art solut... |
Title: Diverse Weight Averaging for Out-of-Distribution Generalization Abstract: Standard neural networks struggle to generalize under distribution shifts. For out-of-distribution generalization in computer vision, the best current approach averages the weights along a training run. In this paper, we propose Diverse We... |
Title: Foundation Posteriors for Approximate Probabilistic Inference Abstract: Probabilistic programs provide an expressive representation language for generative models. Given a probabilistic program, we are interested in the task of posterior inference: estimating a latent variable given a set of observed variables. ... |
Title: RankGen: Improving Text Generation with Large Ranking Models Abstract: Given an input sequence (or prefix), modern language models often assign high probabilities to output sequences that are repetitive, incoherent, or irrelevant to the prefix; as such, model-generated text also contains such artifacts. To addre... |
Title: Robust and Efficient Medical Imaging with Self-Supervision Abstract: Recent progress in Medical Artificial Intelligence (AI) has delivered systems that can reach clinical expert level performance. However, such systems tend to demonstrate sub-optimal "out-of-distribution" performance when evaluated in clinical s... |
Title: HyperAid: Denoising in hyperbolic spaces for tree-fitting and hierarchical clustering Abstract: The problem of fitting distances by tree-metrics has received significant attention in the theoretical computer science and machine learning communities alike, due to many applications in natural language processing, ... |
Title: Flexible Modeling and Multitask Learning using Differentiable Tree Ensembles Abstract: Decision tree ensembles are widely used and competitive learning models. Despite their success, popular toolkits for learning tree ensembles have limited modeling capabilities. For instance, these toolkits support a limited nu... |
Title: Bi-LSTM Scoring Based Similarity Measurement with Agglomerative Hierarchical Clustering (AHC) for Speaker Diarization Abstract: Majority of speech signals across different scenarios are never available with well-defined audio segments containing only a single speaker. A typical conversation between two speakers ... |
Title: k-strip: A novel segmentation algorithm in k-space for the application of skull stripping Abstract: Objectives: Present a novel deep learning-based skull stripping algorithm for magnetic resonance imaging (MRI) that works directly in the information rich k-space. Materials and Methods: Using two datasets from di... |
Title: Extract Dynamic Information To Improve Time Series Modeling: a Case Study with Scientific Workflow Abstract: In modeling time series data, we often need to augment the existing data records to increase the modeling accuracy. In this work, we describe a number of techniques to extract dynamic information about th... |
Title: Parallel and Distributed Graph Neural Networks: An In-Depth Concurrency Analysis Abstract: Graph neural networks (GNNs) are among the most powerful tools in deep learning. They routinely solve complex problems on unstructured networks, such as node classification, graph classification, or link prediction, with h... |
Title: Neural network topological snake models for locating general phase diagrams Abstract: Machine learning for locating phase diagram has received intensive research interest in recent years. However, its application in automatically locating phase diagram is limited to single closed phase boundary. In this paper, i... |
Title: ArabGlossBERT: Fine-Tuning BERT on Context-Gloss Pairs for WSD Abstract: Using pre-trained transformer models such as BERT has proven to be effective in many NLP tasks. This paper presents our work to fine-tune BERT models for Arabic Word Sense Disambiguation (WSD). We treated the WSD task as a sentence-pair bin... |
Title: Dexterous Robotic Manipulation using Deep Reinforcement Learning and Knowledge Transfer for Complex Sparse Reward-based Tasks Abstract: This paper describes a deep reinforcement learning (DRL) approach that won Phase 1 of the Real Robot Challenge (RRC) 2021, and then extends this method to a more difficult manip... |
Title: Metrics of calibration for probabilistic predictions Abstract: Predictions are often probabilities; e.g., a prediction could be for precipitation tomorrow, but with only a 30% chance. Given such probabilistic predictions together with the actual outcomes, "reliability diagrams" help detect and diagnose statistic... |
Title: Semi-Supervised Learning for Image Classification using Compact Networks in the BioMedical Context Abstract: The development of mobile and on the edge applications that embed deep convolutional neural models has the potential to revolutionise biomedicine. However, most deep learning models require computational ... |
Title: Beyond Greedy Search: Tracking by Multi-Agent Reinforcement Learning-based Beam Search Abstract: To track the target in a video, current visual trackers usually adopt greedy search for target object localization in each frame, that is, the candidate region with the maximum response score will be selected as the ... |
Title: Disentangling Active and Passive Cosponsorship in the U.S. Congress Abstract: In the U.S. Congress, legislators can use active and passive cosponsorship to support bills. We show that these two types of cosponsorship are driven by two different motivations: the backing of political colleagues and the backing of ... |
Title: Detect Professional Malicious User with Metric Learning in Recommender Systems Abstract: In e-commerce, online retailers are usually suffering from professional malicious users (PMUs), who utilize negative reviews and low ratings to their consumed products on purpose to threaten the retailers for illegal profits... |
Title: Semi-WTC: A Practical Semi-supervised Framework for Attack Categorization through Weight-Task Consistency Abstract: Supervised learning has been widely used for attack detection, which requires large amounts of high-quality data and labels. However, the data is often imbalanced and sufficient annotations are dif... |
Title: The AI Mechanic: Acoustic Vehicle Characterization Neural Networks Abstract: In a world increasingly dependent on road-based transportation, it is essential to understand vehicles. We introduce the AI mechanic, an acoustic vehicle characterization deep learning system, as an integrated approach using sound captu... |
Title: Self-Consistent Dynamical Field Theory of Kernel Evolution in Wide Neural Networks Abstract: We analyze feature learning in infinite width neural networks trained with gradient flow through a self-consistent dynamical field theory. We construct a collection of deterministic dynamical order parameters which are i... |
Title: Wojood: Nested Arabic Named Entity Corpus and Recognition using BERT Abstract: This paper presents Wojood, a corpus for Arabic nested Named Entity Recognition (NER). Nested entities occur when one entity mention is embedded inside another entity mention. Wojood consists of about 550K Modern Standard Arabic (MSA)... |
Title: Are Graph Representation Learning Methods Robust to Graph Sparsity and Asymmetric Node Information? Abstract: The growing popularity of Graph Representation Learning (GRL) methods has resulted in the development of a large number of models applied to a miscellany of domains. Behind this diversity of domains, the... |
Title: The First Optimal Acceleration of High-Order Methods in Smooth Convex Optimization Abstract: In this paper, we study the fundamental open question of finding the optimal high-order algorithm for solving smooth convex minimization problems. Arjevani et al. (2019) established the lower bound $\Omega\left(\epsilon^... |
Title: Certified Error Control of Candidate Set Pruning for Two-Stage Relevance Ranking Abstract: In information retrieval (IR), candidate set pruning has been commonly used to speed up two-stage relevance ranking. However, such an approach lacks accurate error control and often trades accuracy off against computationa... |
Title: What killed the Convex Booster ? Abstract: A landmark negative result of Long and Servedio established a worst-case spectacular failure of a supervised learning trio (loss, algorithm, model) otherwise praised for its high precision machinery. Hundreds of papers followed up on the two suspected culprits: the loss... |
Title: Focused Adversarial Attacks Abstract: Recent advances in machine learning show that neural models are vulnerable to minimally perturbed inputs, or adversarial examples. Adversarial algorithms are optimization problems that minimize the accuracy of ML models by perturbing inputs, often using a model's loss functi... |
Title: What Is Fairness? Implications For FairML Abstract: A growing body of literature in fairness-aware ML (fairML) aspires to mitigate machine learning (ML)-related unfairness in automated decision making (ADM) by defining metrics that measure fairness of an ML model and by proposing methods that ensure that trained... |
Title: Towards a Theory of Faithfulness: Faithful Explanations of Differentiable Classifiers over Continuous Data Abstract: There is broad agreement in the literature that explanation methods should be faithful to the model that they explain, but faithfulness remains a rather vague term. We revisit faithfulness in the ... |
Title: Improving Robustness against Real-World and Worst-Case Distribution Shifts through Decision Region Quantification Abstract: The reliability of neural networks is essential for their use in safety-critical applications. Existing approaches generally aim at improving the robustness of neural networks to either rea... |
Title: A Topological Approach for Semi-Supervised Learning Abstract: Nowadays, Machine Learning and Deep Learning methods have become the state-of-the-art approach to solve data classification tasks. In order to use those methods, it is necessary to acquire and label a considerable amount of data; however, this is not ... |
Title: Women, artificial intelligence, and key positions in collaboration networks: Towards a more equal scientific ecosystem Abstract: Scientific collaboration in almost every discipline is mainly driven by the need of sharing knowledge, expertise, and pooled resources. Science is becoming more complex which has encou... |
Title: EXACT: How to Train Your Accuracy Abstract: Classification tasks are usually evaluated in terms of accuracy. However, accuracy is discontinuous and cannot be directly optimized using gradient ascent. Popular methods minimize cross-entropy, Hinge loss, or other surrogate losses, which can lead to suboptimal resul... |
Title: CLCNet: Rethinking of Ensemble Modeling with Classification Confidence Network Abstract: In this paper, we propose a Classification Confidence Network (CLCNet) that can determine whether the classification model classifies input samples correctly. It can take a classification result in the form of vector in any ... |
Title: Learning Energy Networks with Generalized Fenchel-Young Losses Abstract: Energy-based models, a.k.a. energy networks, perform inference by optimizing an energy function, typically parametrized by a neural network. This allows one to capture potentially complex relationships between inputs and outputs. To learn t... |
Title: How catastrophic can catastrophic forgetting be in linear regression? Abstract: To better understand catastrophic forgetting, we study fitting an overparameterized linear model to a sequence of tasks with different input distributions. We analyze how much the model forgets the true labels of earlier tasks after ... |
Title: Discovering Dynamic Functional Brain Networks via Spatial and Channel-wise Attention Abstract: Using deep learning models to recognize functional brain networks (FBNs) in functional magnetic resonance imaging (fMRI) has been attracting increasing interest recently. However, most existing work focuses on detectin... |
Title: Learning Graph Structure from Convolutional Mixtures Abstract: Machine learning frameworks such as graph neural networks typically rely on a given, fixed graph to exploit relational inductive biases and thus effectively learn from network data. However, when said graphs are (partially) unobserved, noisy, or dyna... |
Title: Jacobian Granger Causal Neural Networks for Analysis of Stationary and Nonstationary Data Abstract: Granger causality is a commonly used method for uncovering information flow and dependencies in a time series. Here we introduce JGC (Jacobian Granger Causality), a neural network-based approach to Granger causali... |
Title: Provably Precise, Succinct and Efficient Explanations for Decision Trees Abstract: Decision trees (DTs) embody interpretable classifiers. DTs have been advocated for deployment in high-risk applications, but also for explaining other complex classifiers. Nevertheless, recent work has demonstrated that prediction... |
Title: Automatic Spoken Language Identification using a Time-Delay Neural Network Abstract: Closed-set spoken language identification is the task of recognizing the language being spoken in a recorded audio clip from a set of known languages. In this study, a language identification system was built and trained to dist... |
Title: Hybrid Intelligent Testing in Simulation-Based Verification Abstract: Efficient and effective testing for simulation-based hardware verification is challenging. Using constrained random test generation, several millions of tests may be required to achieve coverage goals. The vast majority of tests do not contrib... |
Title: Data Valuation for Offline Reinforcement Learning Abstract: The success of deep reinforcement learning (DRL) hinges on the availability of training data, which is typically obtained via a large number of environment interactions. In many real-world scenarios, costs and risks are associated with gathering these d... |
Title: ODBO: Bayesian Optimization with Search Space Prescreening for Directed Protein Evolution Abstract: Directed evolution is a versatile technique in protein engineering that mimics the process of natural selection by iteratively alternating between mutagenesis and screening in order to search for sequences that op... |
Title: Closing the gap: Exact maximum likelihood training of generative autoencoders using invertible layers Abstract: In this work, we provide an exact likelihood alternative to the variational training of generative autoencoders. We show that VAE-style autoencoders can be constructed using invertible layers, which of... |
Title: Parallel bandit architecture based on laser chaos for reinforcement learning Abstract: Accelerating artificial intelligence by photonics is an active field of study aiming to exploit the unique properties of photons. Reinforcement learning is an important branch of machine learning, and photonic decision-making ... |
Title: Data-driven prediction of Air Traffic Controllers reactions to resolving conflicts Abstract: With the aim to enhance automation in conflict detection and resolution (CD&R) tasks in the Air Traffic Management domain, in this paper we propose deep learning techniques (DL) that can learn models of Air Traffic Contr... |
Title: IFTT-PIN: A PIN-Entry Method Leveraging the Self-Calibration Paradigm Abstract: IFTT-PIN is a self-calibrating version of the PIN-entry method introduced in Roth et al. (2004) [1]. In [1], digits are split into two sets and assigned a color respectively. To communicate their digit, users press the button with th... |
Title: Simple Regularisation for Uncertainty-Aware Knowledge Distillation Abstract: Considering uncertainty estimation of modern neural networks (NNs) is one of the most important steps towards deploying machine learning systems to meaningful real-world applications such as in medicine, finance or autonomous systems. A... |
Title: scICML: Information-theoretic Co-clustering-based Multi-view Learning for the Integrative Analysis of Single-cell Multi-omics data Abstract: Modern high-throughput sequencing technologies have enabled us to profile multiple molecular modalities from the same single cell, providing unprecedented opportunities to ... |
Title: Variational Inference for Bayesian Bridge Regression Abstract: We study the implementation of Automatic Differentiation Variational inference (ADVI) for Bayesian inference on regression models with bridge penalization. The bridge approach uses $\ell_{\alpha}$ norm, with $\alpha \in (0, +\infty)$ to define a pena... |
Title: The Impact of COVID-19 Pandemic on LGBTQ Online Communities Abstract: The COVID-19 pandemic has disproportionately impacted the lives of minorities, such as members of the LGBTQ community (lesbian, gay, bisexual, transgender, and queer) due to pre-existing social disadvantages and health disparities. Although ex... |
Title: Differentially private Riemannian optimization Abstract: In this paper, we study the differentially private empirical risk minimization problem where the parameter is constrained to a Riemannian manifold. We introduce a framework of differentially private Riemannian optimization by adding noise to the Riemannian... |
Title: Spatial Autoregressive Coding for Graph Neural Recommendation Abstract: Graph embedding methods including traditional shallow models and deep Graph Neural Networks (GNNs) have led to promising applications in recommendation. Nevertheless, shallow models especially random-walk-based algorithms fail to adequately ... |
Title: A Boosting Algorithm for Positive-Unlabeled Learning Abstract: Positive-unlabeled (PU) learning deals with binary classification problems when only positive (P) and unlabeled (U) data are available. A lot of PU methods based on linear models and neural networks have been proposed; however, there still lacks stud... |
Title: Neural ODEs with Irregular and Noisy Data Abstract: Measurement noise is an integral part while collecting data of a physical process. Thus, noise removal is necessary to draw conclusions from these data, and it often becomes essential to construct dynamical models using these data. We discuss a methodology to l... |
Title: Nebula-I: A General Framework for Collaboratively Training Deep Learning Models on Low-Bandwidth Cloud Clusters Abstract: The ever-growing model size and scale of compute have attracted increasing interests in training deep learning models over multiple nodes. However, when it comes to training on cloud clusters... |
Title: Personalized Interventions for Online Moderation Abstract: Current online moderation follows a one-size-fits-all approach, where each intervention is applied in the same way to all users. This naive approach is challenged by established socio-behavioral theories and by recent empirical results that showed the li... |
Title: Why only Micro-F1? Class Weighting of Measures for Relation Classification Abstract: Relation classification models are conventionally evaluated using only a single measure, e.g., micro-F1, macro-F1 or AUC. In this work, we analyze weighting schemes, such as micro and macro, for imbalanced datasets. We introduce... |
Title: Neural Network Architecture Beyond Width and Depth Abstract: This paper proposes a new neural network architecture by introducing an additional dimension called height beyond width and depth. Neural network architectures with height, width, and depth as hyperparameters are called three-dimensional architectures.... |
Title: Differential Privacy: What is all the noise about? Abstract: Differential Privacy (DP) is a formal definition of privacy that provides rigorous guarantees against risks of privacy breaches during data processing. It makes no assumptions about the knowledge or computational power of adversaries, and provides an i... |
Title: Learning-based AC-OPF Solvers on Realistic Network and Realistic Loads Abstract: Deep learning approaches for the Alternating Current-Optimal Power Flow (AC-OPF) problem are under active research in recent years. A common shortcoming in this area of research is the lack of a dataset that includes both a realisti... |
Title: Gold-standard solutions to the Schrödinger equation using deep learning: How much physics do we need? Abstract: Finding accurate solutions to the Schr\"odinger equation is the key unsolved challenge of computational chemistry. Given its importance for the development of new chemical compounds, decades of researc... |
Title: Smooth densities and generative modeling with unsupervised random forests Abstract: Density estimation is a fundamental problem in statistics, and any attempt to do so in high dimensions typically requires strong assumptions or complex deep learning architectures. An important application for density estimators ... |
Title: CAMEO: Curiosity Augmented Metropolis for Exploratory Optimal Policies Abstract: Reinforcement Learning has drawn huge interest as a tool for solving optimal control problems. Solving a given problem (task or environment) involves converging towards an optimal policy. However, there might exist multiple optimal ... |
Title: Action Conditioned Tactile Prediction: a case study on slip prediction Abstract: Tactile predictive models can be useful across several robotic manipulation tasks, e.g. robotic pushing, robotic grasping, slip avoidance, and in-hand manipulation. However, available tactile prediction models are mostly studied for... |
Title: Machine learning applications for noisy intermediate-scale quantum computers Abstract: Quantum machine learning has proven to be a fruitful area in which to search for potential applications of quantum computers. This is particularly true for those available in the near term, so called noisy intermediate-scale q... |
Title: Predictive Maintenance using Machine Learning Abstract: Predictive maintenance (PdM) is a concept, which is implemented to effectively manage maintenance plans of the assets by predicting their failures with data driven techniques. In these scenarios, data is collected over a certain period of time to monitor th... |
Title: Transformers as Neural Augmentors: Class Conditional Sentence Generation via Variational Bayes Abstract: Data augmentation methods for Natural Language Processing tasks are explored in recent years, however they are limited and it is hard to capture the diversity on sentence level. Besides, it is not always poss... |
Title: SOL: Reducing the Maintenance Overhead for Integrating Hardware Support into AI Frameworks Abstract: The increased interest in Artificial Intelligence (AI) raised the need for highly optimized and sophisticated AI frameworks. Starting with the Lua-based Torch many frameworks have emerged over time, such as Thean... |
Title: Truncated tensor Schatten p-norm based approach for spatiotemporal traffic data imputation with complicated missing patterns Abstract: Rapid advances in sensor, wireless communication, cloud computing and data science have brought unprecedented amount of data to assist transportation engineers and researchers in... |
Title: Simplifying Node Classification on Heterophilous Graphs with Compatible Label Propagation Abstract: Graph Neural Networks (GNNs) have been predominant for graph learning tasks; however, recent studies showed that a well-known graph algorithm, Label Propagation (LP), combined with a shallow neural network can ach... |
Title: BabyNet: Residual Transformer Module for Birth Weight Prediction on Fetal Ultrasound Video Abstract: Predicting fetal weight at birth is an important aspect of perinatal care, particularly in the context of antenatal management, which includes the planned timing and the mode of delivery. Accurate prediction of w... |
Title: GitRanking: A Ranking of GitHub Topics for Software Classification using Active Sampling Abstract: GitHub is the world's largest host of source code, with more than 150M repositories. However, most of these repositories are not labeled or inadequately so, making it harder for users to find relevant projects. The... |
Title: Multi-DNN Accelerators for Next-Generation AI Systems Abstract: As the use of AI-powered applications widens across multiple domains, so do increase the computational demands. Primary driver of AI technology are the deep neural networks (DNNs). When focusing either on cloud-based systems that serve multiple AI q... |
Title: Continual Pre-Training Mitigates Forgetting in Language and Vision Abstract: Pre-trained models are nowadays a fundamental component of machine learning research. In continual learning, they are commonly used to initialize the model before training on the stream of non-stationary data. However, pre-training is r... |
Title: Bypassing Logits Bias in Online Class-Incremental Learning with a Generative Framework Abstract: Continual learning requires the model to maintain the learned knowledge while learning from a non-i.i.d data stream continually. Due to the single-pass training setting, online continual learning is very challenging,... |
Title: Consistent Interpolating Ensembles via the Manifold-Hilbert Kernel Abstract: Recent research in the theory of overparametrized learning has sought to establish generalization guarantees in the interpolating regime. Such results have been established for a few common classes of methods, but so far not for ensembl... |
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