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Title: Accelerating Frank-Wolfe via Averaging Step Directions Abstract: The Frank-Wolfe method is a popular method in sparse constrained optimization, due to its fast per-iteration complexity. However, the tradeoff is that its worst case global convergence is comparatively slow, and importantly, is fundamentally slower... |
Title: Quadratic models for understanding neural network dynamics Abstract: In this work, we propose using a quadratic model as a tool for understanding properties of wide neural networks in both optimization and generalization. We show analytically that certain deep learning phenomena such as the "catapult phase" from... |
Title: Transition to Linearity of General Neural Networks with Directed Acyclic Graph Architecture Abstract: In this paper we show that feedforward neural networks corresponding to arbitrary directed acyclic graphs undergo transition to linearity as their "width" approaches infinity. The width of these general networks... |
Title: Attributing AUC-ROC to Analyze Binary Classifier Performance Abstract: Area Under the Receiver Operating Characteristic Curve (AUC-ROC) is a popular evaluation metric for binary classifiers. In this paper, we discuss techniques to segment the AUC-ROC along human-interpretable dimensions. AUC-ROC is not an additi... |
Title: Wireless Ad Hoc Federated Learning: A Fully Distributed Cooperative Machine Learning Abstract: Federated learning has allowed training of a global model by aggregating local models trained on local nodes. However, it still takes client-server model, which can be further distributed, fully decentralized, or even ... |
Title: Constrained Monotonic Neural Networks Abstract: Deep neural networks are becoming increasingly popular in approximating arbitrary functions from noisy data. But wider adoption is being hindered by the need to explain such models and to impose additional constraints on them. Monotonicity constraint is one of the ... |
Title: Learning Context-Aware Service Representation for Service Recommendation in Workflow Composition Abstract: As increasingly more software services have been published onto the Internet, it remains a significant challenge to recommend suitable services to facilitate scientific workflow composition. This paper prop... |
Title: Byzantine-Robust Federated Learning with Optimal Statistical Rates and Privacy Guarantees Abstract: We propose Byzantine-robust federated learning protocols with nearly optimal statistical rates. In contrast to prior work, our proposed protocols improve the dimension dependence and achieve a tight statistical ra... |
Title: Graph Neural Networks Intersect Probabilistic Graphical Models: A Survey Abstract: Graphs are a powerful data structure to represent relational data and are widely used to describe complex real-world data structures. Probabilistic Graphical Models (PGMs) have been well-developed in the past years to mathematical... |
Title: MOSPAT: AutoML based Model Selection and Parameter Tuning for Time Series Anomaly Detection Abstract: Organizations leverage anomaly and changepoint detection algorithms to detect changes in user behavior or service availability and performance. Many off-the-shelf detection algorithms, though effective, cannot r... |
Title: BabyBear: Cheap inference triage for expensive language models Abstract: Transformer language models provide superior accuracy over previous models but they are computationally and environmentally expensive. Borrowing the concept of model cascading from computer vision, we introduce BabyBear, a framework for cas... |
Title: Alleviating Robust Overfitting of Adversarial Training With Consistency Regularization Abstract: Adversarial training (AT) has proven to be one of the most effective ways to defend Deep Neural Networks (DNNs) against adversarial attacks. However, the phenomenon of robust overfitting, i.e., the robustness will dr... |
Title: Demand Response Method Considering Multiple Types of Flexible Loads in Industrial Parks Abstract: With the rapid development of the energy internet, the proportion of flexible loads in smart grid is getting much higher than before. It is highly important to model flexible loads based on demand response. Therefor... |
Title: Towards a Defense against Backdoor Attacks in Continual Federated Learning Abstract: Backdoor attacks are a major concern in federated learning (FL) pipelines where training data is sourced from untrusted clients over long periods of time (i.e., continual learning). Preventing such attacks is difficult because d... |
Title: Soft-SVM Regression For Binary Classification Abstract: The binomial deviance and the SVM hinge loss functions are two of the most widely used loss functions in machine learning. While there are many similarities between them, they also have their own strengths when dealing with different types of data. In this ... |
Title: ItemSage: Learning Product Embeddings for Shopping Recommendations at Pinterest Abstract: Learned embeddings for products are an important building block for web-scale e-commerce recommendation systems. At Pinterest, we build a single set of product embeddings called ItemSage to provide relevant recommendations ... |
Title: On the Role of Bidirectionality in Language Model Pre-Training Abstract: Prior work on language model pre-training has explored different architectures and learning objectives, but differences in data, hyperparameters and evaluation make a principled comparison difficult. In this work, we focus on bidirectionali... |
Title: Embedding Neighborhoods Simultaneously t-SNE (ENS-t-SNE) Abstract: We propose an algorithm for visualizing a dataset by embedding it in 3-dimensional Euclidean space based on various given distances between the same pairs of datapoints. Its aim is to find an Embedding which preserves Neighborhoods Simultaneously... |
Title: Semi-Parametric Deep Neural Networks in Linear Time and Memory Abstract: Recent advances in deep learning have been driven by large-scale parametric models, which can be computationally expensive and lack interpretability. Semi-parametric methods query the training set at inference time and can be more compact, ... |
Title: Randomly Initialized One-Layer Neural Networks Make Data Linearly Separable Abstract: Recently, neural networks have been shown to perform exceptionally well in transforming two arbitrary sets into two linearly separable sets. Doing this with a randomly initialized neural network is of immense interest because t... |
Title: Functional Network: A Novel Framework for Interpretability of Deep Neural Networks Abstract: The layered structure of deep neural networks hinders the use of numerous analysis tools and thus the development of its interpretability. Inspired by the success of functional brain networks, we propose a novel framewor... |
Title: RCC-GAN: Regularized Compound Conditional GAN for Large-Scale Tabular Data Synthesis Abstract: This paper introduces a novel generative adversarial network (GAN) for synthesizing large-scale tabular databases which contain various features such as continuous, discrete, and binary. Technically, our GAN belongs to... |
Title: High-Order Pooling for Graph Neural Networks with Tensor Decomposition Abstract: Graph Neural Networks (GNNs) are attracting growing attention due to their effectiveness and flexibility in modeling a variety of graph-structured data. Exiting GNN architectures usually adopt simple pooling operations (e.g., sum, a... |
Title: HiPAL: A Deep Framework for Physician Burnout Prediction Using Activity Logs in Electronic Health Records Abstract: Burnout is a significant public health concern affecting nearly half of the healthcare workforce. This paper presents the first end-to-end deep learning framework for predicting physician burnout b... |
Title: Compressing Deep Graph Neural Networks via Adversarial Knowledge Distillation Abstract: Deep graph neural networks (GNNs) have been shown to be expressive for modeling graph-structured data. Nevertheless, the over-stacked architecture of deep graph models makes it difficult to deploy and rapidly test on mobile o... |
Title: Semi-Supervised Clustering of Sparse Graphs: Crossing the Information-Theoretic Threshold Abstract: The stochastic block model is a canonical random graph model for clustering and community detection on network-structured data. Decades of extensive study on the problem have established many profound results, amo... |
Title: Learning multi-scale functional representations of proteins from single-cell microscopy data Abstract: Protein function is inherently linked to its localization within the cell, and fluorescent microscopy data is an indispensable resource for learning representations of proteins. Despite major developments in mo... |
Title: PCA-Boosted Autoencoders for Nonlinear Dimensionality Reduction in Low Data Regimes Abstract: Autoencoders (AE) provide a useful method for nonlinear dimensionality reduction but are ill-suited for low data regimes. Conversely, Principal Component Analysis (PCA) is data-efficient but is limited to linear dimensi... |
Title: Throwing Away Data Improves Worst-Class Error in Imbalanced Classification Abstract: Class imbalances pervade classification problems, yet their treatment differs in theory and practice. On the one hand, learning theory instructs us that \emph{more data is better}, as sample size relates inversely to the average... |
Title: Information Propagation by Composited Labels in Natural Language Processing Abstract: In natural language processing (NLP), labeling on regions of text, such as words, sentences and paragraphs, is a basic task. In this paper, label is defined as map between mention of entity in a region on text and context of en... |
Title: FlexiBERT: Are Current Transformer Architectures too Homogeneous and Rigid? Abstract: The existence of a plethora of language models makes the problem of selecting the best one for a custom task challenging. Most state-of-the-art methods leverage transformer-based models (e.g., BERT) or their variants. Training ... |
Title: A Natural Language Processing Pipeline for Detecting Informal Data References in Academic Literature Abstract: Discovering authoritative links between publications and the datasets that they use can be a labor-intensive process. We introduce a natural language processing pipeline that retrieves and reviews publi... |
Title: Deep Representations for Time-varying Brain Datasets Abstract: Finding an appropriate representation of dynamic activities in the brain is crucial for many downstream applications. Due to its highly dynamic nature, temporally averaged fMRI (functional magnetic resonance imaging) can only provide a narrow view of... |
Title: Machine Learning for Electricity Market Clearing Abstract: This paper seeks to design a machine learning twin of the optimal power flow (OPF) optimization, which is used in market-clearing procedures by wholesale electricity markets. The motivation for the proposed approach stems from the need to obtain the digi... |
Title: Generalization Gap in Amortized Inference Abstract: The ability of likelihood-based probabilistic models to generalize to unseen data is central to many machine learning applications such as lossless compression. In this work, we study the generalizations of a popular class of probabilistic models - the Variatio... |
Title: DOGE-Train: Discrete Optimization on GPU with End-to-end Training Abstract: We present a fast, scalable, data-driven approach for solving linear relaxations of 0-1 integer linear programs using a graph neural network. Our solver is based on the Lagrange decomposition based algorithm FastDOG (Abbas et al. (2022))... |
Title: Forecasting of Non-Stationary Sales Time Series Using Deep Learning Abstract: The paper describes the deep learning approach for forecasting non-stationary time series with using time trend correction in a neural network model. Along with the layers for predicting sales values, the neural network model includes ... |
Title: Identifying Patient-Specific Root Causes of Disease Abstract: Complex diseases are caused by a multitude of factors that may differ between patients. As a result, hypothesis tests comparing all patients to all healthy controls can detect many significant variables with inconsequential effect sizes. A few highly ... |
Title: Utilizing Language-Image Pretraining for Efficient and Robust Bilingual Word Alignment Abstract: Word translation without parallel corpora has become feasible, rivaling the performance of supervised methods. Recent findings have shown that the accuracy and robustness of unsupervised word translation (UWT) can be... |
Title: uGLAD: Sparse graph recovery by optimizing deep unrolled networks Abstract: Probabilistic Graphical Models (PGMs) are generative models of complex systems. They rely on conditional independence assumptions between variables to learn sparse representations which can be visualized in a form of a graph. Such models... |
Title: Interpretable Feature Engineering for Time Series Predictors using Attention Networks Abstract: Regression problems with time-series predictors are common in banking and many other areas of application. In this paper, we use multi-head attention networks to develop interpretable features and use them to achieve ... |
Title: PrivFairFL: Privacy-Preserving Group Fairness in Federated Learning Abstract: Group fairness ensures that the outcome of machine learning (ML) based decision making systems are not biased towards a certain group of people defined by a sensitive attribute such as gender or ethnicity. Achieving group fairness in F... |
Title: BolT: Fused Window Transformers for fMRI Time Series Analysis Abstract: Functional magnetic resonance imaging (fMRI) enables examination of inter-regional interactions in the brain via functional connectivity (FC) analyses that measure the synchrony between the temporal activations of separate regions. Given the... |
Title: Quasi Black-Box Variational Inference with Natural Gradients for Bayesian Learning Abstract: We develop an optimization algorithm suitable for Bayesian learning in complex models. Our approach relies on natural gradient updates within a general black-box framework for efficient training with limited model-specif... |
Title: Identifying (anti-)skyrmions while they form Abstract: We use a Convolutional Neural Network (CNN) to identify the relevant features in the thermodynamical phases of a simulated three-dimensional spin-lattice system with ferromagnetic and Dzyaloshinskii-Moriya (DM) interactions. Such features include (anti-)skyr... |
Title: Contrastive and Non-Contrastive Self-Supervised Learning Recover Global and Local Spectral Embedding Methods Abstract: Self-Supervised Learning (SSL) surmises that inputs and pairwise positive relationships are enough to learn meaningful representations. Although SSL has recently reached a milestone: outperformi... |
Title: Computationally Efficient Horizon-Free Reinforcement Learning for Linear Mixture MDPs Abstract: Recent studies have shown that episodic reinforcement learning (RL) is not more difficult than contextual bandits, even with a long planning horizon and unknown state transitions. However, these results are limited to... |
Title: Orchestra: Unsupervised Federated Learning via Globally Consistent Clustering Abstract: Federated learning is generally used in tasks where labels are readily available (e.g., next word prediction). Relaxing this constraint requires design of unsupervised learning techniques that can support desirable properties... |
Title: What Makes Data-to-Text Generation Hard for Pretrained Language Models? Abstract: Expressing natural language descriptions of structured facts or relations -- data-to-text generation (D2T) -- increases the accessibility of structured knowledge repositories. Previous work shows that pre-trained language models(PL... |
Title: Domain Adaptation for Memory-Efficient Dense Retrieval Abstract: Dense retrievers encode documents into fixed dimensional embeddings. However, storing all the document embeddings within an index produces bulky indexes which are expensive to serve. Recently, BPR (Yamada et al., 2021) and JPQ (Zhan et al., 2021a) ... |
Title: Flexible Diffusion Modeling of Long Videos Abstract: We present a framework for video modeling based on denoising diffusion probabilistic models that produces long-duration video completions in a variety of realistic environments. We introduce a generative model that can at test-time sample any arbitrary subset ... |
Title: Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding Abstract: We present Imagen, a text-to-image diffusion model with an unprecedented degree of photorealism and a deep level of language understanding. Imagen builds on the power of large transformer language models in understanding tex... |
Title: Robust and Agnostic Learning of Conditional Distributional Treatment Effects Abstract: The conditional average treatment effect (CATE) is the best point prediction of individual causal effects given individual baseline covariates and can help personalize treatments. However, as CATE only reflects the (conditiona... |
Title: Conditional Supervised Contrastive Learning for Fair Text Classification Abstract: Contrastive representation learning has gained much attention due to its superior performance in learning representations from both image and sequential data. However, the learned representations could potentially lead to performa... |
Title: Learning differential equations from data Abstract: Differential equations are used to model problems that originate in disciplines such as physics, biology, chemistry, and engineering. In recent times, due to the abundance of data, there is an active search for data-driven methods to learn Differential equation... |
Title: Exposing Outlier Exposure: What Can Be Learned From Few, One, and Zero Outlier Images Abstract: Traditionally anomaly detection (AD) is treated as an unsupervised problem utilizing only normal samples due to the intractability of characterizing everything that looks unlike the normal data. However, it has recent... |
Title: Rethinking Streaming Machine Learning Evaluation Abstract: While most work on evaluating machine learning (ML) models focuses on computing accuracy on batches of data, tracking accuracy alone in a streaming setting (i.e., unbounded, timestamp-ordered datasets) fails to appropriately identify when models are perf... |
Title: Exploiting the Curvature of Feasible Sets for Faster Projection-Free Online Learning Abstract: In this paper, we develop new efficient projection-free algorithms for Online Convex Optimization (OCO). Online Gradient Descent (OGD) is an example of a classical OCO algorithm that guarantees the optimal $O(\sqrt{T})... |
Title: Advanced Transient Diagnostic with Ensemble Digital Twin Modeling Abstract: The use of machine learning (ML) model as digital-twins for reduced-order-modeling (ROM) in lieu of system codes has grown traction over the past few years. However, due to the complex and non-linear nature of nuclear reactor transients ... |
Title: CELEST: Federated Learning for Globally Coordinated Threat Detection Abstract: The cyber-threat landscape has evolved tremendously in recent years, with new threat variants emerging daily, and large-scale coordinated campaigns becoming more prevalent. In this study, we propose CELEST (CollaborativE LEarning for ... |
Title: What is Your Metric Telling You? Evaluating Classifier Calibration under Context-Specific Definitions of Reliability Abstract: Classifier calibration has received recent attention from the machine learning community due both to its practical utility in facilitating decision making, as well as the observation tha... |
Title: Data augmentation for efficient learning from parametric experts Abstract: We present a simple, yet powerful data-augmentation technique to enable data-efficient learning from parametric experts for reinforcement and imitation learning. We focus on what we call the policy cloning setting, in which we use online ... |
Title: Overfitting in quantum machine learning and entangling dropout Abstract: The ultimate goal in machine learning is to construct a model function that has a generalization capability for unseen dataset, based on given training dataset. If the model function has too much expressibility power, then it may overfit to... |
Title: Federated Distillation based Indoor Localization for IoT Networks Abstract: Federated distillation (FD) paradigm has been recently proposed as a promising alternative to federated learning (FL) especially in wireless sensor networks with limited communication resources. However, all state-of-the art FD algorithm... |
Title: SiPRNet: End-to-End Learning for Single-Shot Phase Retrieval Abstract: Traditional optimization algorithms have been developed to deal with the phase retrieval problem. However, multiple measurements with different random or non-random masks are needed for giving a satisfactory performance. This brings a burden ... |
Title: Informed Pre-Training on Prior Knowledge Abstract: When training data is scarce, the incorporation of additional prior knowledge can assist the learning process. While it is common to initialize neural networks with weights that have been pre-trained on other large data sets, pre-training on more concise forms o... |
Title: Logical Reasoning with Span Predictions: Span-level Logical Atoms for Interpretable and Robust NLI Models Abstract: Current Natural Language Inference (NLI) models achieve impressive results, sometimes outperforming humans when evaluating on in-distribution test sets. However, as these models are known to learn ... |
Title: Spreading Factor and RSSI for Localization in LoRa Networks: A Deep Reinforcement Learning Approach Abstract: Recent advancements in Internet of Things (IoT) technologies have resulted in a tightening of requirements from various applications including localization in LoRa networks. To address the growing demand... |
Title: Instance-Based Uncertainty Estimation for Gradient-Boosted Regression Trees Abstract: We propose Instance-Based Uncertainty estimation for Gradient-boosted regression trees~(IBUG), a simple method for extending any GBRT point predictor to produce probabilistic predictions. IBUG computes a non-parametric distribu... |
Title: Deep-learning-based prediction of nanoparticle phase transitions during in situ transmission electron microscopy Abstract: We develop the machine learning capability to predict a time sequence of in-situ transmission electron microscopy (TEM) video frames based on the combined long-short-term-memory (LSTM) algor... |
Title: Variable-Input Deep Operator Networks Abstract: Existing architectures for operator learning require that the number and locations of sensors (where the input functions are evaluated) remain the same across all training and test samples, significantly restricting the range of their applicability. We address this... |
Title: Causal Machine Learning for Healthcare and Precision Medicine Abstract: Causal machine learning (CML) has experienced increasing popularity in healthcare. Beyond the inherent capabilities of adding domain knowledge into learning systems, CML provides a complete toolset for investigating how a system would react ... |
Title: Fine-Grained Counting with Crowd-Sourced Supervision Abstract: Crowd-sourcing is an increasingly popular tool for image analysis in animal ecology. Computer vision methods that can utilize crowd-sourced annotations can help scale up analysis further. In this work we study the potential to do so on the challengin... |
Title: Generic bounds on the approximation error for physics-informed (and) operator learning Abstract: We propose a very general framework for deriving rigorous bounds on the approximation error for physics-informed neural networks (PINNs) and operator learning architectures such as DeepONets and FNOs as well as for p... |
Title: StreamingQA: A Benchmark for Adaptation to New Knowledge over Time in Question Answering Models Abstract: Knowledge and language understanding of models evaluated through question answering (QA) has been usually studied on static snapshots of knowledge, like Wikipedia. However, our world is dynamic, evolves over... |
Title: Markedness in Visual Semantic AI Abstract: We evaluate the state-of-the-art multimodal "visual semantic" model CLIP ("Contrastive Language Image Pretraining") for biases related to the marking of age, gender, and race or ethnicity. Given the option to label an image as "a photo of a person" or to select a label ... |
Title: Learned Digital Back-Propagation for Dual-Polarization Dispersion Managed Systems Abstract: Digital back-propagation (DBP) and learned DBP (LDBP) are proposed for nonlinearity mitigation in WDM dual-polarization dispersion-managed systems. LDBP achieves Q-factor improvement of 1.8 dB and 1.2 dB, respectively, ov... |
Title: Exploring the limits of multifunctionality across different reservoir computers Abstract: Multifunctional neural networks are capable of performing more than one task without changing any network connections. In this paper we explore the performance of a continuous-time, leaky-integrator, and next-generation `re... |
Title: User Clustering for Rate Splitting using Machine Learning Abstract: Hierarchical Rate Splitting (HRS) schemes proposed in recent years have shown to provide significant improvements in exploiting spatial diversity in wireless networks and provide high throughput for all users while minimising interference among ... |
Title: Statistical inference as Green's functions Abstract: Statistical inference from data is foundational task in science. Recently, it receives growing attention for its central role in inference systems of primary interest in data science, artificial intelligence, or machine learning. However, the understanding of ... |
Title: Graph-Based Methods for Discrete Choice Abstract: Choices made by individuals have widespread impacts--for instance, people choose between political candidates to vote for, between social media posts to share, and between brands to purchase--moreover, data on these choices are increasingly abundant. Discrete cho... |
Title: Chaotic Regularization and Heavy-Tailed Limits for Deterministic Gradient Descent Abstract: Recent studies have shown that gradient descent (GD) can achieve improved generalization when its dynamics exhibits a chaotic behavior. However, to obtain the desired effect, the step-size should be chosen sufficiently la... |
Title: Capacity Bounds for the DeepONet Method of Solving Differential Equations Abstract: In recent times machine learning methods have made significant advances in becoming a useful tool for analyzing physical systems. A particularly active area in this theme has been "physics informed machine learning" [1] which foc... |
Title: POLTER: Policy Trajectory Ensemble Regularization for Unsupervised Reinforcement Learning Abstract: The goal of Unsupervised Reinforcement Learning (URL) is to find a reward-agnostic prior policy on a task domain, such that the sample-efficiency on supervised downstream tasks is improved. Although agents initial... |
Title: ScholarBERT: Bigger is Not Always Better Abstract: Transformer-based masked language models trained on general corpora, such as BERT and RoBERTa, have shown impressive performance on various downstream tasks. Increasingly, researchers are "finetuning" these models to improve performance on domain-specific tasks.... |
Title: FedSA: Accelerating Intrusion Detection in Collaborative Environments with Federated Simulated Annealing Abstract: Fast identification of new network attack patterns is crucial for improving network security. Nevertheless, identifying an ongoing attack in a heterogeneous network is a non-trivial task. Federated ... |
Title: ImGCL: Revisiting Graph Contrastive Learning on Imbalanced Node Classification Abstract: Graph contrastive learning (GCL) has attracted a surge of attention due to its superior performance for learning node/graph representations without labels. However, in practice, unlabeled nodes for the given graph usually fo... |
Title: Towards automatic detection of wildlife trade using machine vision models Abstract: Unsustainable trade in wildlife is one of the major threats affecting the global biodiversity crisis. An important part of the trade now occurs on the internet, especially on digital marketplaces and social media. Automated metho... |
Title: Learning heterophilious edge to drop: A general framework for boosting graph neural networks Abstract: Graph Neural Networks (GNNs) aim at integrating node contents with graph structure to learn nodes/graph representations. Nevertheless, it is found that most of existing GNNs do not work well on data with high h... |
Title: Active Learning Through a Covering Lens Abstract: Deep active learning aims to reduce the annotation cost for deep neural networks, which are notoriously data-hungry. Until recently, deep active learning methods struggled in the low-budget regime, where only a small amount of samples are annotated. The situation... |
Title: Privacy-preserving Data Filtering in Federated Learning Using Influence Approximation Abstract: Federated Learning by nature is susceptible to low-quality, corrupted, or even malicious data that can severely degrade the quality of the learned model. Traditional techniques for data valuation cannot be applied as ... |
Title: Training Efficient CNNS: Tweaking the Nuts and Bolts of Neural Networks for Lighter, Faster and Robust Models Abstract: Deep Learning has revolutionized the fields of computer vision, natural language understanding, speech recognition, information retrieval and more. Many techniques have evolved over the past de... |
Title: SelfReformer: Self-Refined Network with Transformer for Salient Object Detection Abstract: The global and local contexts significantly contribute to the integrity of predictions in Salient Object Detection (SOD). Unfortunately, existing methods still struggle to generate complete predictions with fine details. T... |
Title: Neural Copula: A unified framework for estimating generic high-dimensional Copula functions Abstract: The Copula is widely used to describe the relationship between the marginal distribution and joint distribution of random variables. The estimation of high-dimensional Copula is difficult, and most existing solu... |
Title: Tyger: Task-Type-Generic Active Learning for Molecular Property Prediction Abstract: How to accurately predict the properties of molecules is an essential problem in AI-driven drug discovery, which generally requires a large amount of annotation for training deep learning models. Annotating molecules, however, i... |
Title: Memory-enriched computation and learning in spiking neural networks through Hebbian plasticity Abstract: Memory is a key component of biological neural systems that enables the retention of information over a huge range of temporal scales, ranging from hundreds of milliseconds up to years. While Hebbian plastici... |
Title: RL with KL penalties is better viewed as Bayesian inference Abstract: Reinforcement learning (RL) is frequently employed in fine-tuning large language models (LMs), such as GPT-3, to penalize them for undesirable features of generated sequences, such as offensiveness, social bias, harmfulness or falsehood. The R... |
Title: Fed-DART and FACT: A solution for Federated Learning in a production environment Abstract: Federated Learning as a decentralized artificial intelligence (AI) solution solves a variety of problems in industrial applications. It enables a continuously self-improving AI, which can be deployed everywhere at the edge... |
Title: What You See is What You Classify: Black Box Attributions Abstract: An important step towards explaining deep image classifiers lies in the identification of image regions that contribute to individual class scores in the model's output. However, doing this accurately is a difficult task due to the black-box nat... |
Title: Deep Image Retrieval is not Robust to Label Noise Abstract: Large-scale datasets are essential for the success of deep learning in image retrieval. However, manual assessment errors and semi-supervised annotation techniques can lead to label noise even in popular datasets. As previous works primarily studied ann... |
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