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Title: Fix Bugs with Transformer through a Neural-Symbolic Edit Grammar Abstract: We introduce NSEdit (neural-symbolic edit), a novel Transformer-based code repair method. Given only the source code that contains bugs, NSEdit predicts an editing sequence that can fix the bugs. The edit grammar is formulated as a regula... |
Title: METRO: Efficient Denoising Pretraining of Large Scale Autoencoding Language Models with Model Generated Signals Abstract: We present an efficient method of pretraining large-scale autoencoding language models using training signals generated by an auxiliary model. Originated in ELECTRA, this training strategy ha... |
Title: Wassmap: Wasserstein Isometric Mapping for Image Manifold Learning Abstract: In this paper, we propose Wasserstein Isometric Mapping (Wassmap), a parameter-free nonlinear dimensionality reduction technique that provides solutions to some drawbacks in existing global nonlinear dimensionality reduction algorithms ... |
Title: Joint Coreset Construction and Quantization for Distributed Machine Learning Abstract: Coresets are small, weighted summaries of larger datasets, aiming at providing provable error bounds for machine learning (ML) tasks while significantly reducing the communication and computation costs. To achieve a better tra... |
Title: Sketching Algorithms and Lower Bounds for Ridge Regression Abstract: We give a sketching-based iterative algorithm that computes $1+\varepsilon$ approximate solutions for the ridge regression problem $\min_x \|{Ax-b}\|_2^2 +\lambda\|{x}\|_2^2$ where $A \in \mathbb{R}^{n \times d}$ with $d \ge n$. Our algorithm, ... |
Title: Second Order Regret Bounds Against Generalized Expert Sequences under Partial Bandit Feedback Abstract: We study the problem of expert advice under partial bandit feedback setting and create a sequential minimax optimal algorithm. Our algorithm works with a more general partial monitoring setting, where, in cont... |
Title: Achieving Representative Data via Convex Hull Feasibility Sampling Algorithms Abstract: Sampling biases in training data are a major source of algorithmic biases in machine learning systems. Although there are many methods that attempt to mitigate such algorithmic biases during training, the most direct and obvi... |
Title: A deep learning algorithm for reducing false positives in screening mammography Abstract: Screening mammography improves breast cancer outcomes by enabling early detection and treatment. However, false positive callbacks for additional imaging from screening exams cause unnecessary procedures, patient anxiety, a... |
Title: GM-TOuNN: Graded Multiscale Topology Optimization using Neural Networks Abstract: Multiscale topology optimization (M-TO) entails generating an optimal global topology, and an optimal set of microstructures at a smaller scale, for a physics-constrained problem. With the advent of additive manufacturing, M-TO has... |
Title: Multifidelity deep neural operators for efficient learning of partial differential equations with application to fast inverse design of nanoscale heat transport Abstract: Deep neural operators can learn operators mapping between infinite-dimensional function spaces via deep neural networks and have become an eme... |
Title: Time Series of Non-Additive Metrics: Identification and Interpretation of Contributing Factors of Variance by Linear Decomposition Abstract: The research paper addresses linear decomposition of time series of non-additive metrics that allows for the identification and interpretation of contributing factors (inpu... |
Title: HASA: Hybrid Architecture Search with Aggregation Strategy for Echinococcosis Classification and Ovary Segmentation in Ultrasound Images Abstract: Different from handcrafted features, deep neural networks can automatically learn task-specific features from data. Due to this data-driven nature, they have achieved... |
Title: Leveraging convergence behavior to balance conflicting tasks in multi-task learning Abstract: Multi-Task Learning is a learning paradigm that uses correlated tasks to improve performance generalization. A common way to learn multiple tasks is through the hard parameter sharing approach, in which a single archite... |
Title: SNP2Vec: Scalable Self-Supervised Pre-Training for Genome-Wide Association Study Abstract: Self-supervised pre-training methods have brought remarkable breakthroughs in the understanding of text, image, and speech. Recent developments in genomics has also adopted these pre-training methods for genome understandi... |
Title: LSTM-Autoencoder based Anomaly Detection for Indoor Air Quality Time Series Data Abstract: Anomaly detection for indoor air quality (IAQ) data has become an important area of research as the quality of air is closely related to human health and well-being. However, traditional statistics and shallow machine lear... |
Title: Control-oriented meta-learning Abstract: Real-time adaptation is imperative to the control of robots operating in complex, dynamic environments. Adaptive control laws can endow even nonlinear systems with good trajectory tracking performance, provided that any uncertain dynamics terms are linearly parameterizabl... |
Title: Learning Convolutional Neural Networks in the Frequency Domain Abstract: Convolutional neural network (CNN) has achieved impressive success in computer vision during the past few decades. The image convolution operation helps CNNs to get good performance on image-related tasks. However, the image convolution has... |
Title: Improving Top-K Decoding for Non-Autoregressive Semantic Parsing via Intent Conditioning Abstract: Semantic parsing (SP) is a core component of modern virtual assistants like Google Assistant and Amazon Alexa. While sequence-to-sequence-based auto-regressive (AR) approaches are common for conversational semantic... |
Title: HCFL: A High Compression Approach for Communication-Efficient Federated Learning in Very Large Scale IoT Networks Abstract: Federated learning (FL) is a new artificial intelligence concept that enables Internet-of-Things (IoT) devices to learn a collaborative model without sending the raw data to centralized nod... |
Title: Supplementation of deep neural networks with simplified physics-based features to increase model prediction accuracy Abstract: To improve predictive models for STEM applications, supplemental physics-based features computed from input parameters are introduced into single and multiple layers of a deep neural net... |
Title: Multimodal spatiotemporal graph neural networks for improved prediction of 30-day all-cause hospital readmission Abstract: Measures to predict 30-day readmission are considered an important quality factor for hospitals as accurate predictions can reduce the overall cost of care by identifying high risk patients ... |
Title: Learning Task-Aware Energy Disaggregation: a Federated Approach Abstract: We consider the problem of learning the energy disaggregation signals for residential load data. Such task is referred as non-intrusive load monitoring (NILM), and in order to find individual devices' power consumption profiles based on ag... |
Title: Sign Bit is Enough: A Learning Synchronization Framework for Multi-hop All-reduce with Ultimate Compression Abstract: Traditional one-bit compressed stochastic gradient descent can not be directly employed in multi-hop all-reduce, a widely adopted distributed training paradigm in network-intensive high-performan... |
Title: YOLO-Pose: Enhancing YOLO for Multi Person Pose Estimation Using Object Keypoint Similarity Loss Abstract: We introduce YOLO-pose, a novel heatmap-free approach for joint detection, and 2D multi-person pose estimation in an image based on the popular YOLO object detection framework. Existing heatmap based two-st... |
Title: deep-significance - Easy and Meaningful Statistical Significance Testing in the Age of Neural Networks Abstract: A lot of Machine Learning (ML) and Deep Learning (DL) research is of an empirical nature. Nevertheless, statistical significance testing (SST) is still not widely used. This endangers true progress, a... |
Title: Stream-based Active Learning with Verification Latency in Non-stationary Environments Abstract: Data stream classification is an important problem in the field of machine learning. Due to the non-stationary nature of the data where the underlying distribution changes over time (concept drift), the model needs to... |
Title: The Vision of Self-Evolving Computing Systems Abstract: Computing systems are omnipresent; their sustainability has become crucial for our society. A key aspect of this sustainability is the ability of computing systems to cope with the continuous change they face, ranging from dynamic operating conditions, to c... |
Title: MARF: Multiscale Adaptive-switch Random Forest for Leg Detection with 2D Laser Scanners Abstract: For the 2D laser-based tasks, e.g., people detection and people tracking, leg detection is usually the first step. Thus, it carries great weight in determining the performance of people detection and people tracking... |
Title: Surface Similarity Parameter: A New Machine Learning Loss Metric for Oscillatory Spatio-Temporal Data Abstract: Supervised machine learning approaches require the formulation of a loss functional to be minimized in the training phase. Sequential data are ubiquitous across many fields of research, and are often t... |
Title: ULF: Unsupervised Labeling Function Correction using Cross-Validation for Weak Supervision Abstract: A way to overcome expensive and time-consuming manual data labeling is weak supervision - automatic annotation of data samples via a predefined set of labeling functions (LFs), rule-based mechanisms that generate... |
Title: Program Analysis of Probabilistic Programs Abstract: Probabilistic programming is a growing area that strives to make statistical analysis more accessible, by separating probabilistic modelling from probabilistic inference. In practice this decoupling is difficult. No single inference algorithm can be used as a ... |
Title: Shedding New Light on the Language of the Dark Web Abstract: The hidden nature and the limited accessibility of the Dark Web, combined with the lack of public datasets in this domain, make it difficult to study its inherent characteristics such as linguistic properties. Previous works on text classification of D... |
Title: Gradient boosting for convex cone predict and optimize problems Abstract: Many problems in engineering and statistics involve both predictive forecasting and decision-based optimization. Traditionally, predictive models are optimized independently from the final decision-based optimization problem. In contrast, ... |
Title: Efficient and practical quantum compiler towards multi-qubit systems with deep reinforcement learning Abstract: Efficient quantum compiling tactics greatly enhance the capability of quantum computers to execute complicated quantum algorithms. Due to its fundamental importance, a plethora of quantum compilers has... |
Title: Measurement-based Admission Control in Sliced Networks: A Best Arm Identification Approach Abstract: In sliced networks, the shared tenancy of slices requires adaptive admission control of data flows, based on measurements of network resources. In this paper, we investigate the design of measurement-based admiss... |
Title: Global Counterfactual Explanations: Investigations, Implementations and Improvements Abstract: Counterfactual explanations have been widely studied in explainability, with a range of application dependent methods emerging in fairness, recourse and model understanding. However, the major shortcoming associated wi... |
Title: Sketch guided and progressive growing GAN for realistic and editable ultrasound image synthesis Abstract: Ultrasound (US) imaging is widely used for anatomical structure inspection in clinical diagnosis. The training of new sonographers and deep learning based algorithms for US image analysis usually requires a ... |
Title: Geometric Deep Learning to Identify the Critical 3D Structural Features of the Optic Nerve Head for Glaucoma Diagnosis Abstract: Purpose: The optic nerve head (ONH) undergoes complex and deep 3D morphological changes during the development and progression of glaucoma. Optical coherence tomography (OCT) is the cu... |
Title: Concentration of Random Feature Matrices in High-Dimensions Abstract: The spectra of random feature matrices provide essential information on the conditioning of the linear system used in random feature regression problems and are thus connected to the consistency and generalization of random feature models. Ran... |
Title: EEG-ITNet: An Explainable Inception Temporal Convolutional Network for Motor Imagery Classification Abstract: In recent years, neural networks and especially deep architectures have received substantial attention for EEG signal analysis in the field of brain-computer interfaces (BCIs). In this ongoing research a... |
Title: LEFM-Nets: Learnable Explicit Feature Map Deep Networks for Segmentation of Histopathological Images of Frozen Sections Abstract: Accurate segmentation of medical images is essential for diagnosis and treatment of diseases. These problems are solved by highly complex models, such as deep networks (DN), requiring... |
Title: Finding MNEMON: Reviving Memories of Node Embeddings Abstract: Previous security research efforts orbiting around graphs have been exclusively focusing on either (de-)anonymizing the graphs or understanding the security and privacy issues of graph neural networks. Little attention has been paid to understand the... |
Title: Latent Aspect Detection from Online Unsolicited Customer Reviews Abstract: Within the context of review analytics, aspects are the features of products and services at which customers target their opinions and sentiments. Aspect detection helps product owners and service providers to identify shortcomings and pr... |
Title: The multi-modal universe of fast-fashion: the Visuelle 2.0 benchmark Abstract: We present Visuelle 2.0, the first dataset useful for facing diverse prediction problems that a fast-fashion company has to manage routinely. Furthermore, we demonstrate how the use of computer vision is substantial in this scenario. ... |
Title: Planting Undetectable Backdoors in Machine Learning Models Abstract: Given the computational cost and technical expertise required to train machine learning models, users may delegate the task of learning to a service provider. We show how a malicious learner can plant an undetectable backdoor into a classifier.... |
Title: Solving AC Power Flow with Graph Neural Networks under Realistic Constraints Abstract: In this paper we propose a graph neural network architecture solving the AC power flow problem under realistic constraints. While the energy transition is changing the energy industry to a digitalized and decentralized energy ... |
Title: Medical Application of Geometric Deep Learning for the Diagnosis of Glaucoma Abstract: Purpose: (1) To assess the performance of geometric deep learning (PointNet) in diagnosing glaucoma from a single optical coherence tomography (OCT) 3D scan of the optic nerve head (ONH); (2) To compare its performance to that... |
Title: Interpretability of Machine Learning Methods Applied to Neuroimaging Abstract: Deep learning methods have become very popular for the processing of natural images, and were then successfully adapted to the neuroimaging field. As these methods are non-transparent, interpretability methods are needed to validate t... |
Title: Learning Invariances with Generalised Input-Convex Neural Networks Abstract: Considering smooth mappings from input vectors to continuous targets, our goal is to characterise subspaces of the input domain, which are invariant under such mappings. Thus, we want to characterise manifolds implicitly defined by leve... |
Title: From Environmental Sound Representation to Robustness of 2D CNN Models Against Adversarial Attacks Abstract: This paper investigates the impact of different standard environmental sound representations (spectrograms) on the recognition performance and adversarial attack robustness of a victim residual convolutio... |
Title: Q-TART: Quickly Training for Adversarial Robustness and in-Transferability Abstract: Raw deep neural network (DNN) performance is not enough; in real-world settings, computational load, training efficiency and adversarial security are just as or even more important. We propose to simultaneously tackle Performanc... |
Title: Exploring the Distributed Knowledge Congruence in Proxy-data-free Federated Distillation Abstract: Federated learning (FL) is a distributed machine learning paradigm in which the server periodically aggregates local model parameters from clients without assembling their private data. Constrained communication an... |
Title: Activation Regression for Continuous Domain Generalization with Applications to Crop Classification Abstract: Geographic variance in satellite imagery impacts the ability of machine learning models to generalise to new regions. In this paper, we model geographic generalisation in medium resolution Landsat-8 sate... |
Title: Epileptic Seizure Risk Assessment by Multi-Channel Imaging of the EEG Abstract: Refractory epileptic patients can suffer a seizure at any moment. Seizure prediction would substantially improve their lives. In this work, based on scalp EEG and its transformation into images, the likelihood of an epileptic seizure... |
Title: LDPC codes: tracking non-stationary channel noise using sequential variational Bayesian estimates Abstract: We present a sequential Bayesian learning method for tracking non-stationary signal-to-noise ratios in LDPC codes using probabilistic graphical models. We represent the LDPC code as a cluster graph using a... |
Title: Ensemble learning using individual neonatal data for seizure detection Abstract: Sharing medical data between institutions is difficult in practice due to data protection laws and official procedures within institutions. Therefore, most existing algorithms are trained on relatively small electroencephalogram (EE... |
Title: Sim-to-Real 6D Object Pose Estimation via Iterative Self-training for Robotic Bin-picking Abstract: In this paper, we propose an iterative self-training framework for sim-to-real 6D object pose estimation to facilitate cost-effective robotic grasping. Given a bin-picking scenario, we establish a photo-realistic ... |
Title: BrainGB: A Benchmark for Brain Network Analysis with Graph Neural Networks Abstract: Mapping the connectome of the human brain using structural or functional connectivity has become one of the most pervasive paradigms for neuroimaging analysis. Recently, Graph Neural Networks (GNNs) motivated from geometric deep... |
Title: Reflective Fiber Faults Detection and Characterization Using Long-Short-Term Memory Abstract: To reduce operation-and-maintenance expenses (OPEX) and to ensure optical network survivability, optical network operators need to detect and diagnose faults in a timely manner and with high accuracy. With the rapid adv... |
Title: Machine Learning-based Anomaly Detection in Optical Fiber Monitoring Abstract: Secure and reliable data communication in optical networks is critical for high-speed Internet. However, optical fibers, serving as the data transmission medium providing connectivity to billons of users worldwide, are prone to a vari... |
Title: Network state Estimation using Raw Video Analysis: vQoS-GAN based non-intrusive Deep Learning Approach Abstract: Content based providers transmits real time complex signal such as video data from one region to another. During this transmission process, the signals usually end up distorted or degraded where the a... |
Title: Streamable Neural Audio Synthesis With Non-Causal Convolutions Abstract: Deep learning models are mostly used in an offline inference fashion. However, this strongly limits the use of these models inside audio generation setups, as most creative workflows are based on real-time digital signal processing. Althoug... |
Title: EvoSTS Forecasting: Evolutionary Sparse Time-Series Forecasting Abstract: In this work, we highlight our novel evolutionary sparse time-series forecasting algorithm also known as EvoSTS. The algorithm attempts to evolutionary prioritize weights of Long Short-Term Memory (LSTM) Network that best minimize the reco... |
Title: A Unified Analysis of Dynamic Interactive Learning Abstract: In this paper we investigate the problem of learning evolving concepts over a combinatorial structure. Previous work by Emamjomeh-Zadeh et al. [2020] introduced dynamics into interactive learning as a way to model non-static user preferences in cluster... |
Title: SemiMultiPose: A Semi-supervised Multi-animal Pose Estimation Framework Abstract: Multi-animal pose estimation is essential for studying animals' social behaviors in neuroscience and neuroethology. Advanced approaches have been proposed to support multi-animal estimation and achieve state-of-the-art performance.... |
Title: Leveraging Natural Learning Processing to Uncover Themes in Clinical Notes of Patients Admitted for Heart Failure Abstract: Heart failure occurs when the heart is not able to pump blood and oxygen to support other organs in the body as it should. Treatments include medications and sometimes hospitalization. Pati... |
Title: Learning and controlling the source-filter representation of speech with a variational autoencoder Abstract: Understanding and controlling latent representations in deep generative models is a challenging yet important problem for analyzing, transforming and generating various types of data. In speech processing... |
Title: Generative power of a protein language model trained on multiple sequence alignments Abstract: Computational models starting from large ensembles of evolutionarily related protein sequences capture a representation of protein families and learn constraints associated to protein structure and function. They thus ... |
Title: Exploring Dual Encoder Architectures for Question Answering Abstract: Dual encoders have been used for question-answering (QA) and information retrieval (IR) tasks with good results. There are two major types of dual encoders, Siamese Dual Encoders (SDE), with parameters shared across two encoders, and Asymmetri... |
Title: MIMO Channel Estimation using Score-Based Generative Models Abstract: Channel estimation is a critical task in multiple-input multiple-output digital communications that has effects on end-to-end system performance. In this work, we introduce a novel approach for channel estimation using deep score-based generat... |
Title: Learning Optimal Dynamic Treatment Regimes Using Causal Tree Methods in Medicine Abstract: Dynamic treatment regimes (DTRs) are used in medicine to tailor sequential treatment decisions to patients by considering patient heterogeneity. Common methods for learning optimal DTRs, however, have shortcomings: they ar... |
Title: Reinforcement Learning Policy Recommendation for Interbank Network Stability Abstract: In this paper we analyze the effect of a policy recommendation on the performances of an artificial interbank market. Financial institutions stipulate lending agreements following a public recommendation and their individual i... |
Title: Scalable and Robust Self-Learning for Skill Routing in Large-Scale Conversational AI Systems Abstract: Skill routing is an important component in large-scale conversational systems. In contrast to traditional rule-based skill routing, state-of-the-art systems use a model-based approach to enable natural conversa... |
Title: Accelerated Policy Learning with Parallel Differentiable Simulation Abstract: Deep reinforcement learning can generate complex control policies, but requires large amounts of training data to work effectively. Recent work has attempted to address this issue by leveraging differentiable simulators. However, inher... |
Title: Masked Siamese Networks for Label-Efficient Learning Abstract: We propose Masked Siamese Networks (MSN), a self-supervised learning framework for learning image representations. Our approach matches the representation of an image view containing randomly masked patches to the representation of the original unmas... |
Title: CLUES: A Benchmark for Learning Classifiers using Natural Language Explanations Abstract: Supervised learning has traditionally focused on inductive learning by observing labeled examples of a task. In contrast, humans have the ability to learn new concepts from language. Here, we explore training zero-shot clas... |
Title: Neighborhood Attention Transformer Abstract: We present Neighborhood Attention Transformer (NAT), an efficient, accurate and scalable hierarchical transformer that works well on both image classification and downstream vision tasks. It is built upon Neighborhood Attention (NA), a simple and flexible attention me... |
Title: Tight Bounds for Quantum State Certification with Incoherent Measurements Abstract: We consider the problem of quantum state certification, where we are given the description of a mixed state $\sigma \in \mathbb{C}^{d \times d}$, $n$ copies of a mixed state $\rho \in \mathbb{C}^{d \times d}$, and $\varepsilon > ... |
Title: Any-resolution Training for High-resolution Image Synthesis Abstract: Generative models operate at fixed resolution, even though natural images come in a variety of sizes. As high-resolution details are downsampled away, and low-resolution images are discarded altogether, precious supervision is lost. We argue t... |
Title: A Level Set Theory for Neural Implicit Evolution under Explicit Flows Abstract: Coordinate-based neural networks parameterizing implicit surfaces have emerged as efficient representations of geometry. They effectively act as parametric level sets with the zero-level set defining the surface of interest. We prese... |
Title: Spatio-Temporal Analysis of Transformer based Architecture for Attention Estimation from EEG Abstract: For many years now, understanding the brain mechanism has been a great research subject in many different fields. Brain signal processing and especially electroencephalogram (EEG) has recently known a growing i... |
Title: Diagnosing and Fixing Manifold Overfitting in Deep Generative Models Abstract: Likelihood-based, or explicit, deep generative models use neural networks to construct flexible high-dimensional densities. This formulation directly contradicts the manifold hypothesis, which states that observed data lies on a low-d... |
Title: Active Learning for Regression and Classification by Inverse Distance Weighting Abstract: This paper proposes an active learning algorithm for solving regression and classification problems based on inverse-distance weighting functions for selecting the feature vectors to query. The algorithm has the following f... |
Title: Relaxing Equivariance Constraints with Non-stationary Continuous Filters Abstract: Equivariances provide useful inductive biases in neural network modeling, with the translation equivariance of convolutional neural networks being a canonical example. Equivariances can be embedded in architectures through weight-... |
Title: Brazilian Court Documents Clustered by Similarity Together Using Natural Language Processing Approaches with Transformers Abstract: Recent advances in Artificial intelligence (AI) have leveraged promising results in solving complex problems in the area of Natural Language Processing (NLP), being an important too... |
Title: Testing distributional assumptions of learning algorithms Abstract: There are many important high dimensional function classes that have fast agnostic learning algorithms when strong assumptions on the distribution of examples can be made, such as Gaussianity or uniformity over the domain. But how can one be suf... |
Title: Hierarchical Embedded Bayesian Additive Regression Trees Abstract: We propose a simple yet powerful extension of Bayesian Additive Regression Trees which we name Hierarchical Embedded BART (HE-BART). The model allows for random effects to be included at the terminal node level of a set of regression trees, makin... |
Title: Alternating Mahalanobis Distance Minimization for Stable and Accurate CP Decomposition Abstract: CP decomposition (CPD) is prevalent in chemometrics, signal processing, data mining and many more fields. While many algorithms have been proposed to compute the CPD, alternating least squares (ALS) remains one of th... |
Title: Causal Disentanglement with Network Information for Debiased Recommendations Abstract: Recommender systems aim to recommend new items to users by learning user and item representations. In practice, these representations are highly entangled as they consist of information about multiple factors, including user's... |
Title: Learning two-phase microstructure evolution using neural operators and autoencoder architectures Abstract: Phase-field modeling is an effective mesoscale method for capturing the evolution dynamics of materials, e.g., in spinodal decomposition of a two-phase mixture. However, the accuracy of high-fidelity phase ... |
Title: Physics-Aware Recurrent Convolutional (PARC) Neural Networks to Assimilate Meso-scale Reactive Mechanics of Energetic Materials Abstract: The thermomechanical properties of energetic materials (EM) are known to be a function of their microscopic structures, i.e., morphological configurations of crystals and pore... |
Title: Harnessing Interpretable Machine Learning for Origami Feature Design and Pattern Selection Abstract: Engineering design of origami systems is challenging because comparing different origami patterns requires using categorical features and evaluating multi-physics behavior targets introduces multi-objective probl... |
Title: Robotic and Generative Adversarial Attacks in Offline Writer-independent Signature Verification Abstract: This study explores how robots and generative approaches can be used to mount successful false-acceptance adversarial attacks on signature verification systems. Initially, a convolutional neural network topo... |
Title: Minimizing Control for Credit Assignment with Strong Feedback Abstract: The success of deep learning attracted interest in whether the brain learns hierarchical representations using gradient-based learning. However, current biologically plausible methods for gradient-based credit assignment in deep neural netwo... |
Title: Methodical Advice Collection and Reuse in Deep Reinforcement Learning Abstract: Reinforcement learning (RL) has shown great success in solving many challenging tasks via use of deep neural networks. Although using deep learning for RL brings immense representational power, it also causes a well-known sample-inef... |
Title: Causal Transformer for Estimating Counterfactual Outcomes Abstract: Estimating counterfactual outcomes over time from observational data is relevant for many applications (e.g., personalized medicine). Yet, state-of-the-art methods build upon simple long short-term memory (LSTM) networks, thus rendering inferenc... |
Title: Convergence and Implicit Regularization Properties of Gradient Descent for Deep Residual Networks Abstract: We prove linear convergence of gradient descent to a global minimum for the training of deep residual networks with constant layer width and smooth activation function. We further show that the trained wei... |
Title: auton-survival: an Open-Source Package for Regression, Counterfactual Estimation, Evaluation and Phenotyping with Censored Time-to-Event Data Abstract: Applications of machine learning in healthcare often require working with time-to-event prediction tasks including prognostication of an adverse event, re-hospit... |
Title: Characterizing the Efficiency vs. Accuracy Trade-off for Long-Context NLP Models Abstract: With many real-world applications of Natural Language Processing (NLP) comprising of long texts, there has been a rise in NLP benchmarks that measure the accuracy of models that can handle longer input sequences. However, ... |
Title: The training response law explains how deep neural networks learn Abstract: Deep neural network is the widely applied technology in this decade. In spite of the fruitful applications, the mechanism behind that is still to be elucidated. We study the learning process with a very simple supervised learning encodin... |
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