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Found 192 papers, 47 papers with code
Building Goal-oriented Document-grounded Dialogue Systems
In this paper, we describe our systems for solving the two Doc2Dial shared task: knowledge identification and response generation.
A Three-step Method for Multi-Hop Inference Explanation Regeneration
Then, a selection module is employed to choose those most relative facts in an autoregressive manner, giving a preliminary order for the retrieved facts.
Collaborative learning of images and geometrics for predicting isocitrate dehydrogenase status of glioma
Further, the collaborative learning model achieves better performance than either the CNN or the GNN alone.
Generalized DataWeighting via Class-Level Gradient Manipulation
To be specific, GDW unrolls the loss gradient to class-level gradients by the chain rule and reweights the flow of each gradient separately.
Leveraging Intrinsic Gradient Information for Further Training of Differentiable Machine Learning Models
Designing models that produce accurate predictions is the fundamental objective of machine learning (ML).
Interval-valued q-Rung Orthopair Fuzzy Choquet Integral Operators and Its Application in Group Decision Making
It is more flexible for decision makers to evaluate by interval-valued q-rung orthopair fuzzy set (IVq-ROFS), which offers fuzzy decision-making more applicational space.
Fairness-aware Online Price Discrimination with Nonparametric Demand Models
To handle this general class, we propose a soft fairness constraint and develop the dynamic pricing policy that achieves $\tilde{O}(T^{4/5})$ regret.
On the Complexity of Dynamic Submodular Maximization
We study dynamic algorithms for the problem of maximizing a monotone submodular function over a stream of $n$ insertions and deletions.
Generalized Data Weighting via Class-level Gradient Manipulation
To be specific, GDW unrolls the loss gradient to class-level gradients by the chain rule and reweights the flow of each gradient separately.
Brain-inspired feature exaggeration in generative replay for continual learning
The catastrophic forgetting of previously learnt classes is one of the main obstacles to the successful development of a reliable and accurate generative continual learning model.
PAC-Bayes Information Bottleneck
In this work, we build a new IB based on the trade-off between the accuracy and complexity of learned weights of NNs.
Deep Learning based Model-free Robust Load Restoration to Enhance Bulk System Resilience with Wind Power Penetration
This paper proposes a new deep learning (DL) based model-free robust method for bulk system on-line load restoration with high penetration of wind power.
Differential Privacy in Personalized Pricing with Nonparametric Demand Models
In the recent decades, the advance of information technology and abundant personal data facilitate the application of algorithmic personalized pricing.
Risk-Adjusted Valuation for Real Option Decisions
We propose a risk-adjusted valuation model to facilitate investors' subjective decision making, in response to the market valuation of an investment opportunity.
Graph-MVP: Multi-View Prototypical Contrastive Learning for Multiplex Graphs
To address these challenges, we propose a novel Graph Multi-View Prototypical (Graph-MVP) framework to extract node embeddings on multiplex graphs.
Predicting isocitrate dehydrogenase mutation status in glioma using structural brain networks and graph neural networks
Here we propose a method to predict IDH mutation using GNN, based on the structural brain network of patients.
Adaptive unsupervised learning with enhanced feature representation for intra-tumor partitioning and survival prediction for glioblastoma
In this paper, we propose an adaptive unsupervised learning approach for efficient MRI intra-tumor partitioning and glioblastoma survival prediction.
CONNER: A Cascade Count and Measurement Extraction Tool for Scientific Discourse
Accordingly, we propose CONNER, a cascade count and measurement extraction tool that can identify entities and the corresponding relations in a two-step pipeline model.
Pruning Ternary Quantization
We propose pruning ternary quantization (PTQ), a simple, yet effective, symmetric ternary quantization method.
Adversarial Robustness of Probabilistic Network Embedding for Link Prediction
However, these methods lack transparency when compared to simpler baselines, and as a result their robustness against adversarial attacks is a possible point of concern: could one or a few small adversarial modifications to the network have a large impact on the link prediction performance when using a network embedding model?
PRGC: Potential Relation and Global Correspondence Based Joint Relational Triple Extraction
Joint extraction of entities and relations from unstructured texts is a crucial task in information extraction.
PRASEMap: A Probabilistic Reasoning and Semantic Embedding based Knowledge Graph Alignment System
Knowledge Graph (KG) alignment aims at finding equivalent entities and relations (i. e., mappings) between two KGs.
Field Embedding: A Unified Grain-Based Framework for Word Representation
Word representations empowered with additional linguistic information have been widely studied and proved to outperform traditional embeddings.
Bridging the Gap Between Practice and PAC-Bayes Theory in Few-Shot Meta-Learning
Despite recent advances in its theoretical understanding, there still remains a significant gap in the ability of existing PAC-Bayesian theories on meta-learning to explain performance improvements in the few-shot learning setting, where the number of training examples in the target tasks is severely limited.
OntoEA: Ontology-guided Entity Alignment via Joint Knowledge Graph Embedding
Semantic embedding has been widely investigated for aligning knowledge graph (KG) entities.
Unsupervised Knowledge Graph Alignment by Probabilistic Reasoning and Semantic Embedding
Knowledge Graph (KG) alignment is to discover the mappings (i. e., equivalent entities, relations, and others) between two KGs.
Accent Recognition with Hybrid Phonetic Features
The performance of voice-controlled systems is usually influenced by accented speech.
Interventional Aspect-Based Sentiment Analysis
Recent neural-based aspect-based sentiment analysis approaches, though achieving promising improvement on benchmark datasets, have reported suffering from poor robustness when encountering confounder such as non-target aspects.
Imperfect also Deserves Reward: Multi-Level and Sequential Reward Modeling for Better Dialog Management
For task-oriented dialog systems, training a Reinforcement Learning (RL) based Dialog Management module suffers from low sample efficiency and slow convergence speed due to the sparse rewards in RL. To solve this problem, many strategies have been proposed to give proper rewards when training RL, but their rewards lack interpretability and cannot accurately estimate the distribution of state-action pairs in real dialogs.
FEW-SHOTLEARNING WITH WEAK SUPERVISION
Few-shot meta-learning methods aim to learn the common structure shared across a set of tasks to facilitate learning new tasks with small amounts of data.
BrainNetGAN: Data augmentation of brain connectivity using generative adversarial network for dementia classification
The proposed BrainNetGAN is a generative adversarial network variant to augment the brain structural connectivity matrices for binary dementia classification tasks.
No Discounted-Regret Learning in Adversarial Bandits with Delays
Using these bounds, we show that EXP3 and FKM have no discounted-regret even for $d_{t}=O\left(t\log t\right)$.
Bayesian Meta-Learning for Few-Shot Policy Adaptation Across Robotic Platforms
Our results show that the proposed method can successfully adapt a trained policy to different robotic platforms with novel physical parameters and the superiority of our meta-learning algorithm compared to state-of-the-art methods for the introduced few-shot policy adaptation problem.
Variance Reduced Median-of-Means Estimator for Byzantine-Robust Distributed Inference
Based on the proposed VRMOM estimator, we develop a general distributed inference algorithm that is robust against Byzantine failures.
Lifelong Learning based Disease Diagnosis on Clinical Notes
Current deep learning based disease diagnosis systems usually fall short in catastrophic forgetting, i. e., directly fine-tuning the disease diagnosis model on new tasks usually leads to abrupt decay of performance on previous tasks.
Learning a Product Relevance Model from Click-Through Data in E-Commerce
We propose a novel way to consider samples of different relevance confidence, and come up with a new training objective to learn a robust relevance model with desirable score distribution.
Does Probabilistic Constellation Shaping Benefit IM-DD Systems without Optical Amplifiers?
Probabilistic constellation shaping (PCS) has been widely applied to amplified coherent optical transmissions owing to its shaping gain over the uniform signaling and fine-grained rate adaptation to the underlying fiber channel condition.
Online Statistical Inference for Stochastic Optimization via Kiefer-Wolfowitz Methods
We first present the asymptotic distribution for the Polyak-Ruppert-averaging type Kiefer-Wolfowitz (AKW) estimators, whose asymptotic covariance matrices depend on the function-value query complexity and the distribution of search directions.
Monitoring SEIRD model parameters using MEWMA for the COVID-19 pandemic with application to the State of Qatar
During the current COVID-19 pandemic, decision makers are tasked with implementing and evaluating strategies for both treatment and disease prevention.
Methodology Applications
Experimental implementation of precisely tailored light-matter interaction via inverse engineering
Here, we implement fast and robust control for the state preparation and state engineering in a rare-earth ions system.
Quantum Physics
Enquire One's Parent and Child Before Decision: Fully Exploit Hierarchical Structure for Self-Supervised Taxonomy Expansion
Taxonomy is a hierarchically structured knowledge graph that plays a crucial role in machine intelligence.
Denoising Single Voxel Magnetic Resonance Spectroscopy with Deep Learning on Repeatedly Sampled In Vivo Data
Methods: By exploring the multiple sampled data, a deep learning denoising approach is proposed to learn a mapping from the low SNR signal to the high SNR one.
Expectation-Maximization Regularized Deep Learning for Weakly Supervised Tumor Segmentation for Glioblastoma
EMReDL showed to effectively segment the infiltrated tumor from the partially labelled region of potential infiltration.
Adversarial Combinatorial Bandits with General Non-linear Reward Functions
{The adversarial combinatorial bandit with general non-linear reward is an important open problem in bandit literature, and it is still unclear whether there is a significant gap from the case of linear reward, stochastic bandit, or semi-bandit feedback.}
Conditional Diffusion for Interactive Segmentation
In click-based interactive segmentation, the mask extraction process is dictated by positive/negative user clicks; however, most existing methods do not fully exploit the user cues, requiring excessive numbers of clicks for satisfactory results.
KETG: A Knowledge Enhanced Text Generation Framework
Embedding logical knowledge information into text generation is a challenging NLP task.
Smoothed Gaussian Mixture Models for Video Classification and Recommendation
SGMM represents each video by the parameters of a Gaussian mixture model (GMM) trained for that video.
Full C-Band WDM Transmission of Nonlinearity-Tolerant Probabilistically Shaped QAM over 2824-km Dispersion-Managed Fiber
By tailoring probabilistic constellation shaping (PCS) for nonlinearity tolerance, we experimentally demonstrate up to 1. 1 dB increase in signal-to-noise ratio (SNR) and 6. 4% increase in total net data rate (NDR) compared to linear-channel-optimized PCS on a 2824-km dispersion-managed wavelength-division multiplexed (WDM) optical fiber link.
Bayesian optimization assisted unsupervised learning for efficient intra-tumor partitioning in MRI and survival prediction for glioblastoma patients
Although successful in tumor sub-region segmentation and survival prediction, radiomics based on machine learning algorithms, is challenged by its robustness, due to the vague intermediate process and track changes.
Enhanced Few-shot Learning for Intrusion Detection in Railway Video Surveillance
Video surveillance is gaining increasing popularity to assist in railway intrusion detection in recent years.
Distant Supervision for E-commerce Query Segmentation via Attention Network
The booming online e-commerce platforms demand highly accurate approaches to segment queries that carry the product requirements of consumers.
Summarizing Chinese Medical Answer with Graph Convolution Networks and Question-focused Dual Attention
It is desirable to generate answer summaries for online search engines, particularly summaries that can reveal direct answers to questions.
An Industry Evaluation of Embedding-based Entity Alignment
Embedding-based entity alignment has been widely investigated in recent years, but most proposed methods still rely on an ideal supervised learning setting with a large number of unbiased seed mappings for training and validation, which significantly limits their usage.
Binary Choice with Asymmetric Loss in a Data-Rich Environment: Theory and an Application to Racial Justice
We study the binary choice problem in a data-rich environment with asymmetric loss functions.
Improving Text Generation Evaluation with Batch Centering and Tempered Word Mover Distance
Recent advances in automatic evaluation metrics for text have shown that deep contextualized word representations, such as those generated by BERT encoders, are helpful for designing metrics that correlate well with human judgements.
DoubleEnsemble: A New Ensemble Method Based on Sample Reweighting and Feature Selection for Financial Data Analysis
Modern machine learning models (such as deep neural networks and boosting decision tree models) have become increasingly popular in financial market prediction, due to their superior capacity to extract complex non-linear patterns.
Privacy-Preserving Dynamic Personalized Pricing with Demand Learning
In this paper, we consider a dynamic pricing problem over $T$ time periods with an \emph{unknown} demand function of posted price and personalized information.
Experimental Demonstration of 4,294,967,296-QAM Based Y-00 Quantum Stream Cipher Carrying 160-Gb/s 16-QAM Signals
We demonstrate a 4, 294, 967, 296-ary quadrature amplitude modulation (QAM) based Y-00 quantum stream cipher system carrying 160-Gb/s 16-QAM signal transmitted over 320-km SSMF.
Finding Influential Instances for Distantly Supervised Relation Extraction
Distant supervision has been demonstrated to be highly beneficial to enhance relation extraction models, but it often suffers from high label noise.
Automated Stroke Rehabilitation Assessment using Wearable Accelerometers in Free-Living Environments
Stroke is known as a major global health problem, and for stroke survivors it is key to monitor the recovery levels.
Information Theoretic Counterfactual Learning from Missing-Not-At-Random Feedback
Counterfactual learning for dealing with missing-not-at-random data (MNAR) is an intriguing topic in the recommendation literature since MNAR data are ubiquitous in modern recommender systems.
Online Disease Self-diagnosis with Inductive Heterogeneous Graph Convolutional Networks
We propose a Healthcare Graph Convolutional Network (HealGCN) to offer disease self-diagnosis service for online users based on Electronic Healthcare Records (EHRs).
On the Sample Complexity of Reinforcement Learning with Policy Space Generalization
To avoid such undesirable dependence on the state and action space sizes, this paper proposes a new notion of eluder dimension for the policy space, which characterizes the intrinsic complexity of policy learning in an arbitrary Markov Decision Process (MDP).
Transformer with Bidirectional Decoder for Speech Recognition
To demonstrate our proposed speech transformer with a bidirectional decoder(STBD), we conduct extensive experiments on the AISHELL-1 dataset.
Variable Skipping for Autoregressive Range Density Estimation
In this paper, we explore a technique, variable skipping, for accelerating range density estimation over deep autoregressive models.
Curves of maximal moduli on K3 surfaces
We prove that if $X$ is a complex projective K3 surface and $g>0$, then there exist infinitely many families of curves of geometric genus $g$ on $X$ with maximal, i. e., $g$-dimensional, variation in moduli.
Algebraic Geometry 14J28, 14N35, 14G17
Human-centered collaborative robots with deep reinforcement learning
We present a reinforcement learning based framework for human-centered collaborative systems.
NeuroCard: One Cardinality Estimator for All Tables
Query optimizers rely on accurate cardinality estimates to produce good execution plans.
COVID-19 Public Opinion and Emotion Monitoring System Based on Time Series Thermal New Word Mining
With the spread and development of new epidemics, it is of great reference value to identify the changing trends of epidemics in public emotions.
Non-iterative Simultaneous Rigid Registration Method for Serial Sections of Biological Tissue
In this paper, we propose a novel non-iterative algorithm to simultaneously estimate optimal rigid transformation for serial section images, which is a key component in volume reconstruction of serial sections of biological tissue.
Learning and Testing Junta Distributions with Subcube Conditioning
The main contribution is an algorithm for finding relevant coordinates in a $k$-junta distribution with subcube conditioning [BC18, CCKLW20].
DAN: A Deformation-Aware Network for Consecutive Biomedical Image Interpolation
The continuity of biological tissue between consecutive biomedical images makes it possible for the video interpolation algorithm, to recover large area defects and tears that are common in biomedical images.
Distributed Estimation for Principal Component Analysis: an Enlarged Eigenspace Analysis
To abandon this eigengap assumption, we consider a new route in our analysis: instead of exactly identifying the top-$L$-dim eigenspace, we show that our estimator is able to cover the targeted top-$L$-dim population eigenspace.
Dimension Independent Generalization Error by Stochastic Gradient Descent
One classical canon of statistics is that large models are prone to overfitting, and model selection procedures are necessary for high dimensional data.
Uncertainty Quantification for Demand Prediction in Contextual Dynamic Pricing
In this paper, using the problem of demand function prediction in dynamic pricing as the motivating example, we study the problem of constructing accurate confidence intervals for the demand function.
Resolution Adaptive Networks for Efficient Inference
Adaptive inference is an effective mechanism to achieve a dynamic tradeoff between accuracy and computational cost in deep networks.
State-Aware Tracker for Real-Time Video Object Segmentation
For higher efficiency, SAT takes advantage of the inter-frame consistency and deals with each target object as a tracklet.
Semantic Segmentation
Semi-Supervised Video Object Segmentation
+1
Fixed-Support Wasserstein Barycenters: Computational Hardness and Fast Algorithm
We study the fixed-support Wasserstein barycenter problem (FS-WBP), which consists in computing the Wasserstein barycenter of $m$ discrete probability measures supported on a finite metric space of size $n$.
Online Covariance Matrix Estimation in Stochastic Gradient Descent
This approach fits in an online setting and takes full advantage of SGD: efficiency in computation and memory.
ALPINE: Active Link Prediction using Network Embedding
Often, the link status of a node pair can be queried, which can be used as additional information by the link prediction algorithm.
On the Fairness of Randomized Trials for Recommendation with Heterogeneous Demographics and Beyond
Observed events in recommendation are consequence of the decisions made by a policy, thus they are usually selectively labeled, namely the data are Missing Not At Random (MNAR), which often causes large bias to the estimate of true outcomes risk.
Correcting Knowledge Base Assertions
The usefulness and usability of knowledge bases (KBs) is often limited by quality issues.
Adaptive Discrete Smoothing for High-Dimensional and Nonlinear Panel Data
In this paper we develop a data-driven smoothing technique for high-dimensional and non-linear panel data models.
EdgeDRNN: Enabling Low-latency Recurrent Neural Network Edge Inference
This paper presents a Gated Recurrent Unit (GRU) based recurrent neural network (RNN) accelerator called EdgeDRNN designed for portable edge computing.
Small-footprint Keyword Spotting with Graph Convolutional Network
Despite the recent successes of deep neural networks, it remains challenging to achieve high precision keyword spotting task (KWS) on resource-constrained devices.
Graph Convolutional Network
Small-Footprint Keyword Spotting
Online EXP3 Learning in Adversarial Bandits with Delayed Feedback
An adversary chooses the cost of each arm in a bounded interval, and a sequence of feedback delays \left\{ d_{t}\right\} that are unknown to the player.
Random Restrictions of High-Dimensional Distributions and Uniformity Testing with Subcube Conditioning
We give a nearly-optimal algorithm for testing uniformity of distributions supported on $\{-1, 1\}^n$, which makes $\tilde O (\sqrt{n}/\varepsilon^2)$ queries to a subcube conditional sampling oracle (Bhattacharyya and Chakraborty (2018)).
Solving Optimization Problems through Fully Convolutional Networks: an Application to the Travelling Salesman Problem
Based on samples of a 10 city TSP, a fully convolutional network (FCN) is used to learn the mapping from a feasible region to an optimal solution.
Thresholding Bandit Problem with Both Duels and Pulls
The Thresholding Bandit Problem (TBP) aims to find the set of arms with mean rewards greater than a given threshold.
Robust Dynamic Assortment Optimization in the Presence of Outlier Customers
The main question investigated in this paper is model mis-specification under the $\varepsilon$-contamination model, which is a fundamental model in robust statistics and machine learning.
Skew-Explore: Learn faster in continuous spaces with sparse rewards
In this work, we introduce an exploration approach based on maximizing the entropy of the visited states while learning a goal-conditioned policy.
Learning Visual Relation Priors for Image-Text Matching and Image Captioning with Neural Scene Graph Generators
In this work, we tackle two fundamental language-and-vision tasks: image-text matching and image captioning, and demonstrate that neural scene graph generators can learn effective visual relation features to facilitate grounding language to visual relations and subsequently improve the two end applications.
Adversarial Feature Training for Generalizable Robotic Visuomotor Control
Deep reinforcement learning (RL) has enabled training action-selection policies, end-to-end, by learning a function which maps image pixels to action outputs.
UER: An Open-Source Toolkit for Pre-training Models
Existing works, including ELMO and BERT, have revealed the importance of pre-training for NLP tasks.
Bayesian posterior repartitioning for nested sampling
We show through numerical examples that this Bayesian PR (BPR) method provides a very robust, self-adapting and computationally efficient `hands-off' solution to the problem of unrepresentative priors in Bayesian inference using NS.
Autoregressive-Model-Based Methods for Online Time Series Prediction with Missing Values: an Experimental Evaluation
In this paper, we focus on online methods for AR-model-based time series prediction with missing values.
Efficient average-case population recovery in the presence of insertions and deletions
In this problem, there is an unknown distribution $\cal{D}$ over $s$ unknown source strings $x^1,\dots, x^s \in \{0, 1\}^n$, and each sample is independently generated by drawing some $x^i$ from $\cal{D}$ and returning an independent trace of $x^i$.
Evaluating Protein Transfer Learning with TAPE
Semi-supervised learning has emerged as an important paradigm in protein modeling due to the high cost of acquiring supervised protein labels, but the current literature is fragmented when it comes to datasets and standardized evaluation techniques.
Distributed High-dimensional Regression Under a Quantile Loss Function
This paper studies distributed estimation and support recovery for high-dimensional linear regression model with heavy-tailed noise.
Detecting Customer Complaint Escalation with Recurrent Neural Networks and Manually-Engineered Features
Consumers dissatisfied with the normal dispute resolution process provided by an e-commerce company{'}s customer service agents have the option of escalating their complaints by filing grievances with a government authority.
Sequence Modeling of Temporal Credit Assignment for Episodic Reinforcement Learning
Recent advances in deep reinforcement learning algorithms have shown great potential and success for solving many challenging real-world problems, including Go game and robotic applications.
Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules
A key challenge in leveraging data augmentation for neural network training is choosing an effective augmentation policy from a large search space of candidate operations.
Ranked #7 on
Image Classification
on SVHN
Deep Unsupervised Cardinality Estimation
To produce a truly usable estimator, we develop a Monte Carlo integration scheme on top of autoregressive models that can efficiently handle range queries with dozens of dimensions or more.
Temporal Collaborative Filtering with Bayesian Probabilistic Tensor Factorization
Motivated by our sales prediction problem, we propose a factor-based algorithm that is able to take time into account.
Tight Regret Bounds for Infinite-armed Linear Contextual Bandits
Linear contextual bandit is an important class of sequential decision making problems with a wide range of applications to recommender systems, online advertising, healthcare, and many other machine learning related tasks.
On Stationary-Point Hitting Time and Ergodicity of Stochastic Gradient Langevin Dynamics
In this paper, using intuitions from stochastic differential equations, we provide a direct analysis for the hitting times of SGLD to the first and second order stationary points.
Towards Pricing Financial Derivatives with an IBM Quantum Computer
Pricing interest-rate financial derivatives is a major problem in finance, in which it is crucial to accurately reproduce the time-evolution of interest rates.
Quantum Physics Mesoscale and Nanoscale Physics
Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks
Here, the challenge is to learn accurate "few-shot" models for classes existing at the tail of the class distribution, for which little data is available.
Context-Based Dynamic Pricing with Online Clustering
By clustering data and identifying products that have similar demand patterns, we utilize sales data from products within the same cluster to improve demand estimation and allow for better pricing decisions.
Flow++: Improving Flow-Based Generative Models with Variational Dequantization and Architecture Design
Flow-based generative models are powerful exact likelihood models with efficient sampling and inference.
Ranked #11 on
Image Generation
on ImageNet 32x32
Bayesian surrogate learning in dynamic simulator-based regression problems
The estimation of unknown values of parameters (or hidden variables, control variables) that characterise a physical system often relies on the comparison of measured data with synthetic data produced by some numerical simulator of the system as the parameter values are varied.
Boundary-Aware Network for Fast and High-Accuracy Portrait Segmentation
Compared with other semantic segmentation tasks, portrait segmentation requires both higher precision and faster inference speed.
Large-Scale Markov Decision Problems via the Linear Programming Dual
Moreover, we propose an efficient algorithm, scaling with the size of the subspace but not the state space, that is able to find a policy with low excess loss relative to the best policy in this class.
Attention-Based Capsule Networks with Dynamic Routing for Relation Extraction
A capsule is a group of neurons, whose activity vector represents the instantiation parameters of a specific type of entity.
Parallelized Interactive Machine Learning on Autonomous Vehicles
In this work, we use a human demonstration approach to speed up training for learning features and use the resulting pre-trained model to replace the neural network in the deep RL Deep Q-Network (DQN), followed by human interaction to further refine the model.
A Method to Facilitate Cancer Detection and Type Classification from Gene Expression Data using a Deep Autoencoder and Neural Network
Here we present models of deep learning (DL) and apply them to gene expression data for the diagnosis and categorization of cancer.
Near-Optimal Policies for Dynamic Multinomial Logit Assortment Selection Models
In this paper we consider the dynamic assortment selection problem under an uncapacitated multinomial-logit (MNL) model.
Distributed Inference for Linear Support Vector Machine
In this paper, we propose a multi-round distributed linear-type (MDL) estimator for conducting inference for linear SVM.
First-order Newton-type Estimator for Distributed Estimation and Inference
The key component in our method is the proposal of a computationally efficient estimator of $\Sigma^{-1} w$, where $\Sigma$ is the population Hessian matrix and $w$ is any given vector.
Meta-Learning for Multi-objective Reinforcement Learning
Multi-objective reinforcement learning (MORL) is the generalization of standard reinforcement learning (RL) approaches to solve sequential decision making problems that consist of several, possibly conflicting, objectives.
Learning from Demonstration in the Wild
Learning from demonstration (LfD) is useful in settings where hand-coding behaviour or a reward function is impractical.
Dynamic Assortment Optimization with Changing Contextual Information
To this end, we develop an upper confidence bound (UCB) based policy and establish the regret bound on the order of $\widetilde O(d\sqrt{T})$, where $d$ is the dimension of the feature and $\widetilde O$ suppresses logarithmic dependence.
Quantile Regression Under Memory Constraint
This paper proposes a computationally efficient method, which only requires an initial QR estimator on a small batch of data and then successively refines the estimator via multiple rounds of aggregations.
Query Tracking for E-commerce Conversational Search: A Machine Comprehension Perspective
With the development of dialog techniques, conversational search has attracted more and more attention as it enables users to interact with the search engine in a natural and efficient manner.
Label-Free Distant Supervision for Relation Extraction via Knowledge Graph Embedding
This paper proposes a label-free distant supervision method, which makes no use of the relation labels under this inadequate assumption, but only uses the prior knowledge derived from the KG to supervise the learning of the classifier directly and softly.
Shape-Enforcing Operators for Point and Interval Estimators
A common problem in econometrics, statistics, and machine learning is to estimate and make inference on functions that satisfy shape restrictions.
Learning to Compose over Tree Structures via POS Tags
Recursive Neural Network (RecNN), a type of models which compose words or phrases recursively over syntactic tree structures, has been proven to have superior ability to obtain sentence representation for a variety of NLP tasks.
Quantized Single-Ion-Channel Hodgkin-Huxley Model for Quantum Neurons
This reduced version is essentially a classical memristor, a resistor whose resistance depends on the history of electric signals that have crossed it, coupled to a voltage source and a capacitor.
Experimental Implementation of a Quantum Autoencoder via Quantum Adders
Quantum autoencoders allow for reducing the amount of resources in a quantum computation by mapping the original Hilbert space onto a reduced space with the relevant information.
Dynamic Assortment Selection under the Nested Logit Models
One key challenge is that utilities of products are unknown to the seller and need to be learned.
A Novel Framework for Recurrent Neural Networks with Enhancing Information Processing and Transmission between Units
This paper proposes a novel framework for recurrent neural networks (RNNs) inspired by the human memory models in the field of cognitive neuroscience to enhance information processing and transmission between adjacent RNNs' units.
An Optimal Policy for Dynamic Assortment Planning Under Uncapacitated Multinomial Logit Models
We further establish the matching lower bound result to show the optimality of our policy.
Deep Reinforcement Learning to Acquire Navigation Skills for Wheel-Legged Robots in Complex Environments
Mobile robot navigation in complex and dynamic environments is a challenging but important problem.
Reinforcement Learning based QoS/QoE-aware Service Function Chaining in Software-Driven 5G Slices
With the ever growing diversity of devices and applications that will be connected to 5G networks, flexible and agile service orchestration with acknowledged QoE that satisfies end-user's functional and QoS requirements is necessary.
Deep Cascade Multi-task Learning for Slot Filling in Online Shopping Assistant
Slot filling is a critical task in natural language understanding (NLU) for dialog systems.
Stacked Cross Attention for Image-Text Matching
Prior work either simply aggregates the similarity of all possible pairs of regions and words without attending differentially to more and less important words or regions, or uses a multi-step attentional process to capture limited number of semantic alignments which is less interpretable.
Ranked #7 on
Cross-Modal Retrieval
on Flickr30k
Some Considerations on Learning to Explore via Meta-Reinforcement Learning
We consider the problem of exploration in meta reinforcement learning.
Web-Scale Responsive Visual Search at Bing
In this paper, we introduce a web-scale general visual search system deployed in Microsoft Bing.
Tracking Multiple Moving Objects Using Unscented Kalman Filtering Techniques
Considering the ambiguity caused by the occlusion among multiple moving objects, we apply an unscented Kalman filtering (UKF) technique for reliable object detection and tracking.
Hierarchical Spatial Transformer Network
Computer vision researchers have been expecting that neural networks have spatial transformation ability to eliminate the interference caused by geometric distortion for a long time.
PixelSNAIL: An Improved Autoregressive Generative Model
Autoregressive generative models consistently achieve the best results in density estimation tasks involving high dimensional data, such as images or audio.
Wasserstein Distributionally Robust Optimization and Variation Regularization
Wasserstein distributionally robust optimization (DRO) has recently achieved empirical success for various applications in operations research and machine learning, owing partly to its regularization effect.
Safer Classification by Synthesis
We posit that a generative approach is the natural remedy for this problem, and propose a method for classification using generative models.
HodgeRank with Information Maximization for Crowdsourced Pairwise Ranking Aggregation
Recently, crowdsourcing has emerged as an effective paradigm for human-powered large scale problem solving in various domains.
Deep Inception-Residual Laplacian Pyramid Networks for Accurate Single Image Super-Resolution
With exploiting contextual information over large image regions in an efficient way, the deep convolutional neural network has shown an impressive performance for single image super-resolution (SR).
Automatic Conflict Detection in Police Body-Worn Audio
Automatic conflict detection has grown in relevance with the advent of body-worn technology, but existing metrics such as turn-taking and overlap are poor indicators of conflict in police-public interactions.
Meta Learning Shared Hierarchies
We develop a metalearning approach for learning hierarchically structured policies, improving sample efficiency on unseen tasks through the use of shared primitives---policies that are executed for large numbers of timesteps.
Deep Imitation Learning for Complex Manipulation Tasks from Virtual Reality Teleoperation
Imitation learning is a powerful paradigm for robot skill acquisition.
Constructing multi-modality and multi-classifier radiomics predictive models through reliable classifier fusion
First, the predictive performance of the model may be reduced when features extracted from an individual imaging modality are blindly combined into a single predictive model.
A Note on a Tight Lower Bound for MNL-Bandit Assortment Selection Models
In this short note we consider a dynamic assortment planning problem under the capacitated multinomial logit (MNL) bandit model.
Review of Tensor Network Contraction Approaches
One goal is to provide a systematic introduction of TN contraction algorithms (motivations, implementations, relations, implications, etc.
Computational Physics Statistical Mechanics Strongly Correlated Electrons Applied Physics Quantum Physics
Non-stationary Stochastic Optimization under $L_{p,q}$-Variation Measures
We consider a non-stationary sequential stochastic optimization problem, in which the underlying cost functions change over time under a variation budget constraint.
An Effective Training Method For Deep Convolutional Neural Network
In this paper, we propose the nonlinearity generation method to speed up and stabilize the training of deep convolutional neural networks.
A Nearly Instance Optimal Algorithm for Top-k Ranking under the Multinomial Logit Model
We study the active learning problem of top-$k$ ranking from multi-wise comparisons under the popular multinomial logit model.
A Simple Neural Attentive Meta-Learner
Deep neural networks excel in regimes with large amounts of data, but tend to struggle when data is scarce or when they need to adapt quickly to changes in the task.
High-fidelity adiabatic quantum computation using the intrinsic Hamiltonian of a spin system: Application to the experimental factorization of 291311
In previous implementations of adiabatic quantum algorithms using spin systems, the average Hamiltonian method with Trotter's formula was conventionally adopted to generate an effective instantaneous Hamiltonian that simulates an adiabatic passage.
Quantum Physics
Parameter Space Noise for Exploration
Combining parameter noise with traditional RL methods allows to combine the best of both worlds.
Adaptive Multiple-Arm Identification
We study the problem of selecting $K$ arms with the highest expected rewards in a stochastic $n$-armed bandit game.
Equivalence Between Policy Gradients and Soft Q-Learning
A partial explanation may be that $Q$-learning methods are secretly implementing policy gradient updates: we show that there is a precise equivalence between $Q$-learning and policy gradient methods in the setting of entropy-regularized reinforcement learning, that "soft" (entropy-regularized) $Q$-learning is exactly equivalent to a policy gradient method.
Evolution Strategies as a Scalable Alternative to Reinforcement Learning
We explore the use of Evolution Strategies (ES), a class of black box optimization algorithms, as an alternative to popular MDP-based RL techniques such as Q-learning and Policy Gradients.
Ranked #2 on
Atari Games
on Atari 2600 Pong
Estimation of the volume of the left ventricle from MRI images using deep neural networks
In 2016, Kaggle organized a competition to estimate the volume of LV from MRI images.
PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modifications
1) We use a discretized logistic mixture likelihood on the pixels, rather than a 256-way softmax, which we find to speed up training.
Bayesian Decision Process for Cost-Efficient Dynamic Ranking via Crowdsourcing
Rank aggregation based on pairwise comparisons over a set of items has a wide range of applications.
Improved Variational Inference with Inverse Autoregressive Flow
The framework of normalizing flows provides a general strategy for flexible variational inference of posteriors over latent variables.
Ranked #29 on
Image Generation
on CIFAR-10
(bits/dimension metric)
On the Recursive Teaching Dimension of VC Classes
This is the first upper bound for $RTD(C)$ that depends only on $VCD(C)$, independent of the size of the concept class $|C|$ and its~domain size $n$.
#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning
In this work, we describe a surprising finding: a simple generalization of the classic count-based approach can reach near state-of-the-art performance on various high-dimensional and/or continuous deep RL benchmarks.
Ranked #1 on
Atari Games
on Atari 2600 Freeway
RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning
The activations of the RNN store the state of the "fast" RL algorithm on the current (previously unseen) MDP.
Variational Lossy Autoencoder
Representation learning seeks to expose certain aspects of observed data in a learned representation that's amenable to downstream tasks like classification.
Statistical Inference for Model Parameters in Stochastic Gradient Descent
Second, for high-dimensional linear regression, using a variant of the SGD algorithm, we construct a debiased estimator of each regression coefficient that is asymptotically normal.
The Role of Context Selection in Object Detection
We investigate the reasons why context in object detection has limited utility by isolating and evaluating the predictive power of different context cues under ideal conditions in which context provided by an oracle.
Improving Variational Inference with Inverse Autoregressive Flow
The framework of normalizing flows provides a general strategy for flexible variational inference of posteriors over latent variables.
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
This paper describes InfoGAN, an information-theoretic extension to the Generative Adversarial Network that is able to learn disentangled representations in a completely unsupervised manner.
Ranked #3 on
Image Generation
on Stanford Dogs
Improved Techniques for Training GANs
We present a variety of new architectural features and training procedures that we apply to the generative adversarial networks (GANs) framework.
Ranked #14 on
Conditional Image Generation
on CIFAR-10
Conditional Image Generation
Semi-Supervised Image Classification
VIME: Variational Information Maximizing Exploration
While there are methods with optimality guarantees in the setting of discrete state and action spaces, these methods cannot be applied in high-dimensional deep RL scenarios.
Competitive analysis of the top-K ranking problem
In particular, we present a linear time algorithm for the top-$K$ problem which has a competitive ratio of $\tilde{O}(\sqrt{n})$; i. e. to solve any instance of top-$K$, our algorithm needs at most $\tilde{O}(\sqrt{n})$ times as many samples needed as the best possible algorithm for that instance (in contrast, all previous known algorithms for the top-$K$ problem have competitive ratios of $\tilde{\Omega}(n)$ or worse).
Building a Dataset for Possessions Identification in Text
Just as industrialization matured from mass production to customization and personalization, so has the Web migrated from generic content to public disclosures of one{'}s most intimately held thoughts, opinions and beliefs.
Benchmarking Deep Reinforcement Learning for Continuous Control
Recently, researchers have made significant progress combining the advances in deep learning for learning feature representations with reinforcement learning.
Ranked #1 on
Continuous Control
on Cart-Pole Balancing
Generating Discriminative Object Proposals via Submodular Ranking
Based on the multi-scale nature of objects in images, our approach is built on top of a hierarchical segmentation.
Sequential Optimization for Efficient High-Quality Object Proposal Generation
We propose a novel object proposal algorithm, BING++, which inherits the virtue of good computational efficiency of BING but significantly improves its proposal localization quality.
Unsupervised Network Pretraining via Encoding Human Design
Our idea is to pretrain the network through the task of replicating the process of hand-designed feature extraction.
Spectral Methods meet EM: A Provably Optimal Algorithm for Crowdsourcing
Crowdsourcing is a popular paradigm for effectively collecting labels at low cost.
Statistical Decision Making for Optimal Budget Allocation in Crowd Labeling
In crowd labeling, a large amount of unlabeled data instances are outsourced to a crowd of workers.
Variance Reduction for Stochastic Gradient Optimization
We demonstrate how to construct the control variate for two practical problems using stochastic gradient optimization.
Evaluating computational models of explanation using human judgments
We evaluate four computational models of explanation in Bayesian networks by comparing model predictions to human judgments.
Classifying and Visualizing Motion Capture Sequences using Deep Neural Networks
The gesture recognition using motion capture data and depth sensors has recently drawn more attention in vision recognition.