text stringlengths 0 4.09k |
|---|
Title: Traffic4cast at NeurIPS 2021 -- Temporal and Spatial Few-Shot Transfer Learning in Gridded Geo-Spatial Processes Abstract: The IARAI Traffic4cast competitions at NeurIPS 2019 and 2020 showed that neural networks can successfully predict future traffic conditions 1 hour into the future on simply aggregated GPS pr... |
Title: Interpretation of Black Box NLP Models: A Survey Abstract: An increasing number of machine learning models have been deployed in domains with high stakes such as finance and healthcare. Despite their superior performances, many models are black boxes in nature which are hard to explain. There are growing efforts... |
Title: RobIn: A Robust Interpretable Deep Network for Schizophrenia Diagnosis Abstract: Schizophrenia is a severe mental health condition that requires a long and complicated diagnostic process. However, early diagnosis is vital to control symptoms. Deep learning has recently become a popular way to analyse and interpr... |
Title: Quantum-Aided Meta-Learning for Bayesian Binary Neural Networks via Born Machines Abstract: Near-term noisy intermediate-scale quantum circuits can efficiently implement implicit probabilistic models in discrete spaces, supporting distributions that are practically infeasible to sample from using classical means... |
Title: Pre-Training Transformer Decoder for End-to-End ASR Model with Unpaired Speech Data Abstract: This paper studies a novel pre-training technique with unpaired speech data, Speech2C, for encoder-decoder based automatic speech recognition (ASR). Within a multi-task learning framework, we introduce two pre-training ... |
Title: Doubly-Robust Estimation for Correcting Position-Bias in Click Feedback for Unbiased Learning to Rank Abstract: Clicks on rankings suffer from position bias: generally items on lower ranks are less likely to be examined - and thus clicked - by users, in spite of their actual preferences between items. The preval... |
Title: Neural Q-learning for solving elliptic PDEs Abstract: Solving high-dimensional partial differential equations (PDEs) is a major challenge in scientific computing. We develop a new numerical method for solving elliptic-type PDEs by adapting the Q-learning algorithm in reinforcement learning. Our "Q-PDE" algorithm... |
Title: Imitate and Repurpose: Learning Reusable Robot Movement Skills From Human and Animal Behaviors Abstract: We investigate the use of prior knowledge of human and animal movement to learn reusable locomotion skills for real legged robots. Our approach builds upon previous work on imitating human or dog Motion Captu... |
Title: Online Learning for Traffic Routing under Unknown Preferences Abstract: In transportation networks, users typically choose routes in a decentralized and self-interested manner to minimize their individual travel costs, which, in practice, often results in inefficient overall outcomes for society. As a result, th... |
Title: Predicting extreme events from data using deep machine learning: when and where Abstract: We develop a deep convolutional neural network (DCNN) based framework for model-free prediction of the occurrence of extreme events both in time ("when") and in space ("where") in nonlinear physical systems of spatial dimen... |
Title: Preventing Over-Smoothing for Hypergraph Neural Networks Abstract: In recent years, hypergraph learning has attracted great attention due to its capacity in representing complex and high-order relationships. However, current neural network approaches designed for hypergraphs are mostly shallow, thus limiting the... |
Title: Recovering models of open quantum systems from data via polynomial optimization: Towards globally convergent quantum system identification Abstract: Current quantum devices suffer imperfections as a result of fabrication, as well as noise and dissipation as a result of coupling to their immediate environments. B... |
Title: Scaling Up Models and Data with $\texttt{t5x}$ and $\texttt{seqio}$ Abstract: Recent neural network-based language models have benefited greatly from scaling up the size of training datasets and the number of parameters in the models themselves. Scaling can be complicated due to various factors including the nee... |
Title: Learning from many trajectories Abstract: We initiate a study of supervised learning from many independent sequences ("trajectories") of non-independent covariates, reflecting tasks in sequence modeling, control, and reinforcement learning. Conceptually, our multi-trajectory setup sits between two traditional se... |
Title: CatIss: An Intelligent Tool for Categorizing Issues Reports using Transformers Abstract: Users use Issue Tracking Systems to keep track and manage issue reports in their repositories. An issue is a rich source of software information that contains different reports including a problem, a request for new features... |
Title: Adversarial Examples in Random Neural Networks with General Activations Abstract: A substantial body of empirical work documents the lack of robustness in deep learning models to adversarial examples. Recent theoretical work proved that adversarial examples are ubiquitous in two-layers networks with sub-exponent... |
Title: Improved Relation Networks for End-to-End Speaker Verification and Identification Abstract: Speaker identification systems in a real-world scenario are tasked to identify a speaker amongst a set of enrolled speakers given just a few samples for each enrolled speaker. This paper demonstrates the effectiveness of ... |
Title: A Derivation of Nesterov's Accelerated Gradient Algorithm from Optimal Control Theory Abstract: Nesterov's accelerated gradient algorithm is derived from first principles. The first principles are founded on the recently-developed optimal control theory for optimization. This theory frames an optimization proble... |
Title: Performative Power Abstract: We introduce the notion of performative power, which measures the ability of a firm operating an algorithmic system, such as a digital content recommendation platform, to steer a population. We relate performative power to the economic theory of market power. Traditional economic con... |
Title: Bayesian optimization with known experimental and design constraints for chemistry applications Abstract: Optimization strategies driven by machine learning, such as Bayesian optimization, are being explored across experimental sciences as an efficient alternative to traditional design of experiment. When combin... |
Title: Automatic Detection of Expressed Emotion from Five-Minute Speech Samples: Challenges and Opportunities Abstract: We present a novel feasibility study on the automatic recognition of Expressed Emotion (EE), a family environment concept based on caregivers speaking freely about their relative/family member. We des... |
Title: VL-InterpreT: An Interactive Visualization Tool for Interpreting Vision-Language Transformers Abstract: Breakthroughs in transformer-based models have revolutionized not only the NLP field, but also vision and multimodal systems. However, although visualization and interpretability tools have become available fo... |
Title: Dual Temperature Helps Contrastive Learning Without Many Negative Samples: Towards Understanding and Simplifying MoCo Abstract: Contrastive learning (CL) is widely known to require many negative samples, 65536 in MoCo for instance, for which the performance of a dictionary-free framework is often inferior becaus... |
Title: Generation and Simulation of Synthetic Datasets with Copulas Abstract: This paper proposes a new method to generate synthetic data sets based on copula models. Our goal is to produce surrogate data resembling real data in terms of marginal and joint distributions. We present a complete and reliable algorithm for... |
Title: Continuous Scene Representations for Embodied AI Abstract: We propose Continuous Scene Representations (CSR), a scene representation constructed by an embodied agent navigating within a space, where objects and their relationships are modeled by continuous valued embeddings. Our method captures feature relations... |
Title: LEAD1.0: A Large-scale Annotated Dataset for Energy Anomaly Detection in Commercial Buildings Abstract: Modern buildings are densely equipped with smart energy meters, which periodically generate a massive amount of time-series data yielding few million data points every day. This data can be leveraged to discov... |
Title: Generating High Fidelity Data from Low-density Regions using Diffusion Models Abstract: Our work focuses on addressing sample deficiency from low-density regions of data manifold in common image datasets. We leverage diffusion process based generative models to synthesize novel images from low-density regions. W... |
Title: R2L: Distilling Neural Radiance Field to Neural Light Field for Efficient Novel View Synthesis Abstract: Recent research explosion on Neural Radiance Field (NeRF) shows the encouraging potential to represent complex scenes with neural networks. One major drawback of NeRF is its prohibitive inference time: Render... |
Title: Audio-Visual Speech Codecs: Rethinking Audio-Visual Speech Enhancement by Re-Synthesis Abstract: Since facial actions such as lip movements contain significant information about speech content, it is not surprising that audio-visual speech enhancement methods are more accurate than their audio-only counterparts.... |
Title: A 23 MW data centre is all you need Abstract: The field of machine learning has achieved striking progress in recent years, witnessing breakthrough results on language modelling, protein folding and nitpickingly fine-grained dog breed classification. Some even succeeded at playing computer games and board games,... |
Title: TransEditor: Transformer-Based Dual-Space GAN for Highly Controllable Facial Editing Abstract: Recent advances like StyleGAN have promoted the growth of controllable facial editing. To address its core challenge of attribute decoupling in a single latent space, attempts have been made to adopt dual-space GAN for... |
Title: A Closer Look at Rehearsal-Free Continual Learning Abstract: Continual learning describes a setting where machine learning models learn novel concepts from continuously shifting training data, while simultaneously avoiding degradation of knowledge on previously seen classes (a phenomenon known as the catastrophi... |
Title: MyStyle: A Personalized Generative Prior Abstract: We introduce MyStyle, a personalized deep generative prior trained with a few shots of an individual. MyStyle allows to reconstruct, enhance and edit images of a specific person, such that the output is faithful to the person's key facial characteristics. Given ... |
Title: DiffSkill: Skill Abstraction from Differentiable Physics for Deformable Object Manipulations with Tools Abstract: We consider the problem of sequential robotic manipulation of deformable objects using tools. Previous works have shown that differentiable physics simulators provide gradients to the environment sta... |
Title: Reproducibility Issues for BERT-based Evaluation Metrics Abstract: Reproducibility is of utmost concern in machine learning and natural language processing (NLP). In the field of natural language generation (especially machine translation), the seminal paper of Post (2018) has pointed out problems of reproducibi... |
Title: Graph-based Active Learning for Semi-supervised Classification of SAR Data Abstract: We present a novel method for classification of Synthetic Aperture Radar (SAR) data by combining ideas from graph-based learning and neural network methods within an active learning framework. Graph-based methods in machine lear... |
Title: Data Sampling Affects the Complexity of Online SGD over Dependent Data Abstract: Conventional machine learning applications typically assume that data samples are independently and identically distributed (i.i.d.). However, practical scenarios often involve a data-generating process that produces highly dependen... |
Title: Improving Adversarial Transferability via Neuron Attribution-Based Attacks Abstract: Deep neural networks (DNNs) are known to be vulnerable to adversarial examples. It is thus imperative to devise effective attack algorithms to identify the deficiencies of DNNs beforehand in security-sensitive applications. To e... |
Title: Leveraging Privacy Profiles to Empower Users in the Digital Society Abstract: Privacy and ethics of citizens are at the core of the concerns raised by our increasingly digital society. Profiling users is standard practice for software applications triggering the need for users, also enforced by laws, to properly... |
Title: Truth Serum: Poisoning Machine Learning Models to Reveal Their Secrets Abstract: We introduce a new class of attacks on machine learning models. We show that an adversary who can poison a training dataset can cause models trained on this dataset to leak significant private details of training points belonging to... |
Title: Efficient Active Learning with Abstention Abstract: The goal of active learning is to achieve the same accuracy achievable by passive learning, while using much fewer labels. Exponential savings in label complexity are provably guaranteed in very special cases, but fundamental lower bounds show that such improve... |
Title: AKF-SR: Adaptive Kalman Filtering-based Successor Representation Abstract: Recent studies in neuroscience suggest that Successor Representation (SR)-based models provide adaptation to changes in the goal locations or reward function faster than model-free algorithms, together with lower computational cost compar... |
Title: SELFIES and the future of molecular string representations Abstract: Artificial intelligence (AI) and machine learning (ML) are expanding in popularity for broad applications to challenging tasks in chemistry and materials science. Examples include the prediction of properties, the discovery of new reaction path... |
Title: SimPO: Simultaneous Prediction and Optimization Abstract: Many machine learning (ML) models are integrated within the context of a larger system as part of a key component for decision making processes. Concretely, predictive models are often employed in estimating the parameters for the input values that are ut... |
Title: rfPhen2Gen: A machine learning based association study of brain imaging phenotypes to genotypes Abstract: Imaging genetic studies aim to find associations between genetic variants and imaging quantitative traits. Traditional genome-wide association studies (GWAS) are based on univariate statistical tests, but wh... |
Title: Efficient Maximal Coding Rate Reduction by Variational Forms Abstract: The principle of Maximal Coding Rate Reduction (MCR$^2$) has recently been proposed as a training objective for learning discriminative low-dimensional structures intrinsic to high-dimensional data to allow for more robust training than stand... |
Title: Support-vector-machine with Bayesian optimization for lithofacies classification using elastic properties Abstract: We investigate an applicability of Bayesian-optimization (BO) to optimize hyperparameters associated with support-vector-machine (SVM) in order to classify facies using elastic properties derived f... |
Title: A quantum learning approach based on Hidden Markov Models for failure scenarios generation Abstract: Finding the failure scenarios of a system is a very complex problem in the field of Probabilistic Safety Assessment (PSA). In order to solve this problem we will use the Hidden Quantum Markov Models (HQMMs) to cr... |
Title: Speech and the n-Back task as a lens into depression. How combining both may allow us to isolate different core symptoms of depression Abstract: Embedded in any speech signal is a rich combination of cognitive, neuromuscular and physiological information. This richness makes speech a powerful signal in relation ... |
Title: Investigating Top-$k$ White-Box and Transferable Black-box Attack Abstract: Existing works have identified the limitation of top-$1$ attack success rate (ASR) as a metric to evaluate the attack strength but exclusively investigated it in the white-box setting, while our work extends it to a more practical black-... |
Title: Tooth Instance Segmentation on Panoramic Dental Radiographs Using U-Nets and Morphological Processing Abstract: Automatic teeth segmentation in panoramic x-ray images is an important research subject of the image analysis in dentistry. In this study, we propose a post-processing stage to obtain a segmentation ma... |
Title: Scalable Whitebox Attacks on Tree-based Models Abstract: Adversarial robustness is one of the essential safety criteria for guaranteeing the reliability of machine learning models. While various adversarial robustness testing approaches were introduced in the last decade, we note that most of them are incompatib... |
Title: Visual-Tactile Multimodality for Following Deformable Linear Objects Using Reinforcement Learning Abstract: Manipulation of deformable objects is a challenging task for a robot. It will be problematic to use a single sensory input to track the behaviour of such objects: vision can be subjected to occlusions, whe... |
Title: VFDS: Variational Foresight Dynamic Selection in Bayesian Neural Networks for Efficient Human Activity Recognition Abstract: In many machine learning tasks, input features with varying degrees of predictive capability are acquired at varying costs. In order to optimize the performance-cost trade-off, one would s... |
Title: Effect of Balancing Data Using Synthetic Data on the Performance of Machine Learning Classifiers for Intrusion Detection in Computer Networks Abstract: Attacks on computer networks have increased significantly in recent days, due in part to the availability of sophisticated tools for launching such attacks as we... |
Title: DBCal: Density Based Calibration of classifier predictions for uncertainty quantification Abstract: Measurement of uncertainty of predictions from machine learning methods is important across scientific domains and applications. We present, to our knowledge, the first such technique that quantifies the uncertain... |
Title: Filter-based Discriminative Autoencoders for Children Speech Recognition Abstract: Children speech recognition is indispensable but challenging due to the diversity of children's speech. In this paper, we propose a filter-based discriminative autoencoder for acoustic modeling. To filter out the influence of vari... |
Title: A Unified Framework for Domain Adaptive Pose Estimation Abstract: While pose estimation is an important computer vision task, it requires expensive annotation and suffers from domain shift. In this paper, we investigate the problem of domain adaptive 2D pose estimation that transfers knowledge learned on a synth... |
Title: Comparative Analysis of Interval Reachability for Robust Implicit and Feedforward Neural Networks Abstract: We use interval reachability analysis to obtain robustness guarantees for implicit neural networks (INNs). INNs are a class of implicit learning models that use implicit equations as layers and have been s... |
Title: A Physics-Guided Neural Operator Learning Approach to Model Biological Tissues from Digital Image Correlation Measurements Abstract: We present a data-driven workflow to biological tissue modeling, which aims to predict the displacement field based on digital image correlation (DIC) measurements under unseen loa... |
Title: Federated Learning Framework Coping with Hierarchical Heterogeneity in Cooperative ITS Abstract: In this paper, we introduce a federated learning framework coping with Hierarchical Heterogeneity (H2-Fed), which can notably enhance the conventional pre-trained deep learning model. The framework exploits data from... |
Title: Perception Prioritized Training of Diffusion Models Abstract: Diffusion models learn to restore noisy data, which is corrupted with different levels of noise, by optimizing the weighted sum of the corresponding loss terms, i.e., denoising score matching loss. In this paper, we show that restoring data corrupted ... |
Title: Rethinking Position Bias Modeling with Knowledge Distillation for CTR Prediction Abstract: Click-through rate (CTR) Prediction is of great importance in real-world online ads systems. One challenge for the CTR prediction task is to capture the real interest of users from their clicked items, which is inherently ... |
Title: Fusing Interpretable Knowledge of Neural Network Learning Agents For Swarm-Guidance Abstract: Neural-based learning agents make decisions using internal artificial neural networks. In certain situations, it becomes pertinent that this knowledge is re-interpreted in a friendly form to both the human and the machi... |
Title: i-Razor: A Neural Input Razor for Feature Selection and Dimension Search in Large-Scale Recommender Systems Abstract: Input features play a crucial role in the predictive performance of DNN-based industrial recommender systems with thousands of categorical and continuous fields from users, items, contexts, and t... |
Title: Selecting task with optimal transport self-supervised learning for few-shot classification Abstract: Few-Shot classification aims at solving problems that only a few samples are available in the training process. Due to the lack of samples, researchers generally employ a set of training tasks from other domains ... |
Title: Predicting Intervention Approval in Clinical Trials through Multi-Document Summarization Abstract: Clinical trials offer a fundamental opportunity to discover new treatments and advance the medical knowledge. However, the uncertainty of the outcome of a trial can lead to unforeseen costs and setbacks. In this st... |
Title: Preventing Distillation-based Attacks on Neural Network IP Abstract: Neural networks (NNs) are already deployed in hardware today, becoming valuable intellectual property (IP) as many hours are invested in their training and optimization. Therefore, attackers may be interested in copying, reverse engineering, or... |
Title: GrowliFlower: An image time series dataset for GROWth analysis of cauLIFLOWER Abstract: This article presents GrowliFlower, a georeferenced, image-based UAV time series dataset of two monitored cauliflower fields of size 0.39 and 0.60 ha acquired in 2020 and 2021. The dataset contains RGB and multispectral ortho... |
Title: Scalable Semi-Modular Inference with Variational Meta-Posteriors Abstract: The Cut posterior and related Semi-Modular Inference are Generalised Bayes methods for Modular Bayesian evidence combination. Analysis is broken up over modular sub-models of the joint posterior distribution. Model-misspecification in mul... |
Title: Building Decision Forest via Deep Reinforcement Learning Abstract: Ensemble learning methods whose base classifier is a decision tree usually belong to the bagging or boosting. However, no previous work has ever built the ensemble classifier by maximizing long-term returns to the best of our knowledge. This pape... |
Title: Graph-in-Graph (GiG): Learning interpretable latent graphs in non-Euclidean domain for biological and healthcare applications Abstract: Graphs are a powerful tool for representing and analyzing unstructured, non-Euclidean data ubiquitous in the healthcare domain. Two prominent examples are molecule property pred... |
Title: On the Efficiency of Integrating Self-supervised Learning and Meta-learning for User-defined Few-shot Keyword Spotting Abstract: User-defined keyword spotting is a task to detect new spoken terms defined by users. This can be viewed as a few-shot learning problem since it is unreasonable for users to define thei... |
Title: Extracting Rules from Neural Networks with Partial Interpretations Abstract: We investigate the problem of extracting rules, expressed in Horn logic, from neural network models. Our work is based on the exact learning model, in which a learner interacts with a teacher (the neural network model) via queries in or... |
Title: Deep Page-Level Interest Network in Reinforcement Learning for Ads Allocation Abstract: A mixed list of ads and organic items is usually displayed in feed and how to allocate the limited slots to maximize the overall revenue is a key problem. Meanwhile, modeling user preference with historical behavior is essent... |
Title: Autoencoder Attractors for Uncertainty Estimation Abstract: The reliability assessment of a machine learning model's prediction is an important quantity for the deployment in safety critical applications. Not only can it be used to detect novel sceneries, either as out-of-distribution or anomaly sample, but it a... |
Title: Autoencoder for Synthetic to Real Generalization: From Simple to More Complex Scenes Abstract: Learning on synthetic data and transferring the resulting properties to their real counterparts is an important challenge for reducing costs and increasing safety in machine learning. In this work, we focus on autoenco... |
Title: DAG-WGAN: Causal Structure Learning With Wasserstein Generative Adversarial Networks Abstract: The combinatorial search space presents a significant challenge to learning causality from data. Recently, the problem has been formulated into a continuous optimization framework with an acyclicity constraint, allowin... |
Title: ECOTS: Early Classification in Open Time Series Abstract: Learning to predict ahead of time events in open time series is challenging. While Early Classification of Time Series (ECTS) tackles the problem of balancing online the accuracy of the prediction with the cost of delaying the decision when the individual... |
Title: Probing Speech Emotion Recognition Transformers for Linguistic Knowledge Abstract: Large, pre-trained neural networks consisting of self-attention layers (transformers) have recently achieved state-of-the-art results on several speech emotion recognition (SER) datasets. These models are typically pre-trained in ... |
Title: CTAB-GAN+: Enhancing Tabular Data Synthesis Abstract: While data sharing is crucial for knowledge development, privacy concerns and strict regulation (e.g., European General Data Protection Regulation (GDPR)) limit its full effectiveness. Synthetic tabular data emerges as alternative to enable data sharing while... |
Title: Structured Pruning Learns Compact and Accurate Models Abstract: The growing size of neural language models has led to increased attention in model compression. The two predominant approaches are pruning, which gradually removes weights from a pre-trained model, and distillation, which trains a smaller compact mo... |
Title: Synthetic Photovoltaic and Wind Power Forecasting Data Abstract: Photovoltaic and wind power forecasts in power systems with a high share of renewable energy are essential in several applications. These include stable grid operation, profitable power trading, and forward-looking system planning. However, there i... |
Title: Oil reservoir recovery factor assessment using Bayesian networks based on advanced approaches to analogues clustering Abstract: The work focuses on the modelling and imputation of oil and gas reservoirs parameters, specifically, the problem of predicting the oil recovery factor (RF) using Bayesian networks (BNs)... |
Title: Transformers for 1D Signals in Parkinson's Disease Detection from Gait Abstract: This paper focuses on the detection of Parkinson's disease based on the analysis of a patient's gait. The growing popularity and success of Transformer networks in natural language processing and image recognition motivated us to de... |
Title: Inverse Design and Experimental Verification of a Bianisotropic Metasurface Using Optimization and Machine Learning Abstract: Electromagnetic metasurfaces have attracted significant interest recently due to their low profile and advantageous applications. Practically, many metasurface designs start with a set of... |
Title: Zero-Shot Cross-lingual Aphasia Detection using Automatic Speech Recognition Abstract: Aphasia is a common speech and language disorder, typically caused by a brain injury or a stroke, that affects millions of people worldwide. Detecting and assessing Aphasia in patients is a difficult, time-consuming process, a... |
Title: Simulator-based explanation and debugging of hazard-triggering events in DNN-based safety-critical systems Abstract: When Deep Neural Networks (DNNs) are used in safety-critical systems, engineers should determine the safety risks associated with DNN errors observed during testing. For DNNs processing images, en... |
Title: Proper Reuse of Image Classification Features Improves Object Detection Abstract: A common practice in transfer learning is to initialize the downstream model weights by pre-training on a data-abundant upstream task. In object detection specifically, the feature backbone is typically initialized with Imagenet cl... |
Title: Robust and Accurate -- Compositional Architectures for Randomized Smoothing Abstract: Randomized Smoothing (RS) is considered the state-of-the-art approach to obtain certifiably robust models for challenging tasks. However, current RS approaches drastically decrease standard accuracy on unperturbed data, severel... |
Title: Accelerating Federated Edge Learning via Topology Optimization Abstract: Federated edge learning (FEEL) is envisioned as a promising paradigm to achieve privacy-preserving distributed learning. However, it consumes excessive learning time due to the existence of straggler devices. In this paper, a novel topology... |
Title: Provable concept learning for interpretable predictions using variational inference Abstract: In safety critical applications, practitioners are reluctant to trust neural networks when no interpretable explanations are available. Many attempts to provide such explanations revolve around pixel level attributions ... |
Title: A Global Modeling Approach for Load Forecasting in Distribution Networks Abstract: Efficient load forecasting is needed to ensure better observability in the distribution networks, whereas such forecasting is made possible by an increasing number of smart meter installations. Because distribution networks includ... |
Title: Physics Informed Shallow Machine Learning for Wind Speed Prediction Abstract: The ability to predict wind is crucial for both energy production and weather forecasting. Mechanistic models that form the basis of traditional forecasting perform poorly near the ground. In this paper, we take an alternative data-dri... |
Title: Separate and conquer heuristic allows robust mining of contrast sets from various types of data Abstract: Identifying differences between groups is one of the most important knowledge discovery problems. The procedure, also known as contrast sets mining, is applied in a wide range of areas like medicine, industr... |
Title: Evaluating the Text-to-SQL Capabilities of Large Language Models Abstract: We perform an empirical evaluation of Text-to-SQL capabilities of the Codex language model. We find that, without any finetuning, Codex is a strong baseline on the Spider benchmark; we also analyze the failure modes of Codex in this setti... |
Title: Learning Disentangled Representations of Negation and Uncertainty Abstract: Negation and uncertainty modeling are long-standing tasks in natural language processing. Linguistic theory postulates that expressions of negation and uncertainty are semantically independent from each other and the content they modify.... |
Title: Estimating the Jacobian matrix of an unknown multivariate function from sample values by means of a neural network Abstract: We describe, implement and test a novel method for training neural networks to estimate the Jacobian matrix $J$ of an unknown multivariate function $F$. The training set is constructed fro... |
Title: Semi-FairVAE: Semi-supervised Fair Representation Learning with Adversarial Variational Autoencoder Abstract: Adversarial learning is a widely used technique in fair representation learning to remove the biases on sensitive attributes from data representations. It usually requires to incorporate the sensitive at... |
Title: Hierarchical model reduction driven by machine learning for parametric advection-diffusion-reaction problems in the presence of noisy data Abstract: We propose a new approach to generate a reliable reduced model for a parametric elliptic problem, in the presence of noisy data. The reference model reduction proce... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.