Search Results for author:
Found 151 papers, 43 papers with code
SPOT: Selective Point Cloud Voting for Better Proposal in Point Cloud Object Detection
The sparsity of point clouds limits deep learning models on capturing long-range dependencies, which makes features extracted by the models ambiguous.
Sentence-aware Adversarial Meta-Learning for Few-Shot Text Classification
Meta-learning has emerged as an effective approach for few-shot text classification.
Noise Learning for Text Classification: A Benchmark
However, we find that noise learning in text classification is relatively underdeveloped: 1. many methods that have been proven effective in the image domain are not explored in text classification, 2. it is difficult to conduct a fair comparison between previous studies as they do experiments in different noise settings.
CORN: Co-Reasoning Network for Commonsense Question Answering
In this paper, we propose a novel model, Co-Reasoning Network (CORN), which adopts a bidirectional multi-level connection structure based on Co-Attention Transformer.
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.
MuLTI: Efficient Video-and-Language Understanding with MultiWay-Sampler and Multiple Choice Modeling
Then, we introduce an attention-based adapter to the encoders, which finetunes the shallow features to improve the model's performance with low GPU memory consumption.
Ranked #1 on
TGIF-Transition
on TGIF-QA
Enhance Local Consistency in Federated Learning: A Multi-Step Inertial Momentum Approach
Federated learning (FL), as a collaborative distributed training paradigm with several edge computing devices under the coordination of a centralized server, is plagued by inconsistent local stationary points due to the heterogeneity of the local partial participation clients, which precipitates the local client-drifts problems and sparks off the unstable and slow convergence, especially on the aggravated heterogeneous dataset.
Complete cavity map of the C. elegans connectome
This study reports for the first time a complete cavity map of C. elegans neural network, developing a new method for mining higher-order structures that can be applied by researchers in neuroscience, network science and other interdisciplinary fields to explore higher-order structural markers of complex systems.
Intelligent Computing: The Latest Advances, Challenges and Future
In recent years, we have witnessed the emergence of intelligent computing, a new computing paradigm that is reshaping traditional computing and promoting digital revolution in the era of big data, artificial intelligence and internet-of-things with new computing theories, architectures, methods, systems, and applications.
Multi-timescale Event Detection in Nonintrusive Load Monitoring based on MDL Principle
Load event detection is the fundamental step for the event-based non-intrusive load monitoring (NILM).
AlignVE: Visual Entailment Recognition Based on Alignment Relations
Such approaches, however, ignore the VE's unique nature of relation inference between the premise and hypothesis.
TorchOpt: An Efficient Library for Differentiable Optimization
TorchOpt further provides a high-performance distributed execution runtime.
Spatial Temporal Graph Convolution with Graph Structure Self-learning for Early MCI Detection
The proposed spatial temporal graph convolution block directly exploits BOLD time series as input features, which provides an interesting view for rsfMRI-based preclinical AD diagnosis.
How Does a Deep Learning Model Architecture Impact Its Privacy?
We investigate several representative model architectures from CNNs to Transformers, and show that Transformers are generally more vulnerable to privacy attacks than CNNs.
Benchmarking Reinforcement Learning Techniques for Autonomous Navigation
Deep reinforcement learning (RL) has brought many successes for autonomous robot navigation.
BOME! Bilevel Optimization Made Easy: A Simple First-Order Approach
Bilevel optimization (BO) is useful for solving a variety of important machine learning problems including but not limited to hyperparameter optimization, meta-learning, continual learning, and reinforcement learning.
GaitFM: Fine-grained Motion Representation for Gait Recognition
Gait recognition aims at identifying individual-specific walking patterns, which is highly dependent on the observation of the different periodic movements of each body part.
Metric Residual Networks for Sample Efficient Goal-Conditioned Reinforcement Learning
Furthermore, we introduce the metric residual network (MRN) that deliberately decomposes the action-value function Q(s, a, g) into the negated summation of a metric plus a residual asymmetric component.
Video object tracking based on YOLOv7 and DeepSORT
Among the current popular MOT methods based on deep learning, Detection Based Tracking (DBT) is the most widely used in industry, and the performance of them depend on their object detection network.
EnvPool: A Highly Parallel Reinforcement Learning Environment Execution Engine
EnvPool is open-sourced at https://github. com/sail-sg/envpool.
Boosting Multi-Label Image Classification with Complementary Parallel Self-Distillation
Then, the MLIC models of fewer categories are trained with these sub-tasks in parallel for respectively learning the joint patterns and the category-specific patterns of labels.
Implicit semantic-based personalized micro-videos recommendation
With the rapid development of mobile Internet and big data, a huge amount of data is generated in the network, but the data that users are really interested in a very small portion.
Effective Mutation Rate Adaptation through Group Elite Selection
GESMR co-evolves a population of solutions and a population of MRs, such that each MR is assigned to a group of solutions.
An Iterative Co-Training Transductive Framework for Zero Shot Learning
How to generate pseudo labels for unseen-class samples and how to use such usually noisy pseudo labels are two critical issues in transductive learning.
Continual Learning and Private Unlearning
As intelligent agents become autonomous over longer periods of time, they may eventually become lifelong counterparts to specific people.
Semantic-diversity transfer network for generalized zero-shot learning via inner disagreement based OOD detector
Firstly, we propose a Semantic-diversity transfer Network (SetNet) addressing the first two limitations, where 1) a multiple-attention architecture and a diversity regularizer are proposed to learn multiple local visual features that are more consistent with semantic attributes and 2) a projector ensemble that geometrically takes diverse local features as inputs is proposed to model visual-semantic relations from diverse local perspectives.
One Parameter Defense -- Defending against Data Inference Attacks via Differential Privacy
The experimental results show the method to be an effective and timely defense against both membership inference and model inversion attacks with no reduction in accuracy.
Label-only Model Inversion Attack: The Attack that Requires the Least Information
In launching a contemporary model inversion attack, the strategies discussed are generally based on either predicted confidence score vectors, i. e., black-box attacks, or the parameters of a target model, i. e., white-box attacks.
Domain-level Pairwise Semantic Interaction for Aspect-Based Sentiment Classification
Aspect-based sentiment classification (ABSC) is a very challenging subtask of sentiment analysis (SA) and suffers badly from the class-imbalance.
Learning a Shield from Catastrophic Action Effects: Never Repeat the Same Mistake
When humans make catastrophic mistakes, they are expected to learn never to repeat them, such as a toddler who touches a hot stove and immediately learns never to do so again.
Modeling User Behavior with Graph Convolution for Personalized Product Search
Our approach can be seamlessly integrated with existing latent space based methods and be potentially applied in any product retrieval method that uses purchase history to model user preferences.
PRIMA: Planner-Reasoner Inside a Multi-task Reasoning Agent
We consider the problem of multi-task reasoning (MTR), where an agent can solve multiple tasks via (first-order) logic reasoning.
STOPS: Short-Term-based Volatility-controlled Policy Search and its Global Convergence
This paper proposes Short-Term VOlatility-controlled Policy Search (STOPS), a novel algorithm that solves risk-averse problems by learning from short-term trajectories instead of long-term trajectories.
HardBoost: Boosting Zero-Shot Learning with Hard Classes
This work is a systematical analysis on the so-called hard class problem in zero-shot learning (ZSL), that is, some unseen classes disproportionally affect the ZSL performances than others, as well as how to remedy the problem by detecting and exploiting hard classes.
A Critical Review of Inductive Logic Programming Techniques for Explainable AI
Despite recent advances in modern machine learning algorithms, the opaqueness of their underlying mechanisms continues to be an obstacle in adoption.
BIG-bench Machine Learning
Explainable artificial intelligence
+2
A Theoretical Understanding of Gradient Bias in Meta-Reinforcement Learning
Gradient-based Meta-RL (GMRL) refers to methods that maintain two-level optimisation procedures wherein the outer-loop meta-learner guides the inner-loop gradient-based reinforcement learner to achieve fast adaptations.
Neural Auto-Curricula in Two-Player Zero-Sum Games
When solving two-player zero-sum games, multi-agent reinforcement learning (MARL) algorithms often create populations of agents where, at each iteration, a new agent is discovered as the best response to a mixture over the opponent population.
Chemical Identification and Indexing in PubMed Articles via BERT and Text-to-Text Approaches
We extend our BERT-based approach to the entity linking task.
Automatic Extraction of Medication Names in Tweets as Named Entity Recognition
Social media posts contain potentially valuable information about medical conditions and health-related behavior.
Text Mining Drug/Chemical-Protein Interactions using an Ensemble of BERT and T5 Based Models
In Track-1 of the BioCreative VII Challenge participants are asked to identify interactions between drugs/chemicals and proteins.
Real-world challenges for multi-agent reinforcement learning in grid-interactive buildings
Building upon prior research that highlighted the need for standardizing environments for building control research, and inspired by recently introduced challenges for real life reinforcement learning control, here we propose a non-exhaustive set of nine real world challenges for reinforcement learning control in grid-interactive buildings.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Artificial intelligence enabled radio propagation for communications-Part I: Channel characterization and antenna-channel optimization
To provide higher data rates, as well as better coverage, cost efficiency, security, adaptability, and scalability, the 5G and beyond 5G networks are developed with various artificial intelligence techniques.
Artificial intelligence enabled radio propagation for communications-Part II: Scenario identification and channel modeling
This two-part paper investigates the application of artificial intelligence (AI) and in particular machine learning (ML) to the study of wireless propagation channels.
Conflict-Averse Gradient Descent for Multi-task Learning
The goal of multi-task learning is to enable more efficient learning than single task learning by sharing model structures for a diverse set of tasks.
BEV-Net: Assessing Social Distancing Compliance by Joint People Localization and Geometric Reasoning
A dataset of crowd scenes with people annotations under a bird's eye view (BEV) and ground truth for metric distances is introduced, and several measures for the evaluation of social distance detection systems are proposed.
Google Landmark Retrieval 2021 Competition Third Place Solution
We present our solutions to the Google Landmark Challenges 2021, for both the retrieval and the recognition tracks.
Trustworthy AI: From Principles to Practices
In this review, we provide AI practitioners with a comprehensive guide for building trustworthy AI systems.
DNN-Opt: An RL Inspired Optimization for Analog Circuit Sizing using Deep Neural Networks
The key contributions of this paper are a novel sample-efficient two-stage deep learning optimization framework leveraging RL actor-critic algorithms, and a recipe to extend it on large industrial circuits using critical device identification.
OOWL500: Overcoming Dataset Collection Bias in the Wild
Second, it is used to show that the augmentation of in the wild datasets, such as ImageNet, with in the lab data, such as OOWL500, can significantly decrease these biases, leading to object recognizers of improved generalization.
TDM: Trustworthy Decision-Making via Interpretability Enhancement
Human-robot interactive decision-making is increasingly becoming ubiquitous, and trust is an influential factor in determining the reliance on autonomy.
Fedlearn-Algo: A flexible open-source privacy-preserving machine learning platform
We use this platform to demonstrate our research and development results on privacy preserving machine learning algorithms.
Superpoint-guided Semi-supervised Semantic Segmentation of 3D Point Clouds
The proposed network is iteratively updated with its predicted pseudo labels, where a superpoint generation module is introduced for extracting superpoints from 3D point clouds, and a pseudo-label optimization module is explored for automatically assigning pseudo labels to the unlabeled points under the constraint of the extracted superpoints.
Rotation Transformation Network: Learning View-Invariant Point Cloud for Classification and Segmentation
Many recent works show that a spatial manipulation module could boost the performances of deep neural networks (DNNs) for 3D point cloud analysis.
Language-Level Semantics Conditioned 3D Point Cloud Segmentation
In this work, a language-level Semantics Conditioned framework for 3D Point cloud segmentation, called SeCondPoint, is proposed, where language-level semantics are introduced to condition the modeling of point feature distribution as well as the pseudo-feature generation, and a feature-geometry-based mixup approach is further proposed to facilitate the distribution learning.
DeepACG: Co-Saliency Detection via Semantic-Aware Contrast Gromov-Wasserstein Distance
This dense correlation volumes enables the network to accurately discover the structured pair-wise pixel similarities among the common salient objects.
Out-of-Scope Intent Detection with Self-Supervision and Discriminative Training
Since the distribution of outlier utterances is arbitrary and unknown in the training stage, existing methods commonly rely on strong assumptions on data distribution such as mixture of Gaussians to make inference, resulting in either complex multi-step training procedures or hand-crafted rules such as confidence threshold selection for outlier detection.
Object-Guided Instance Segmentation With Auxiliary Feature Refinement for Biological Images
To deal with this problem, in this paper, we propose an object-guided instance segmentation method.
Neural Auto-Curricula
When solving two-player zero-sum games, multi-agent reinforcement learning (MARL) algorithms often create populations of agents where, at each iteration, a new agent is discovered as the best response to a mixture over the opponent population.
Coach-Player Multi-Agent Reinforcement Learning for Dynamic Team Composition
Specifically, we 1) adopt the attention mechanism for both the coach and the players; 2) propose a variational objective to regularize learning; and 3) design an adaptive communication method to let the coach decide when to communicate with the players.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
Semi-supervised Long-tailed Recognition using Alternate Sampling
A consistency loss has been introduced to limit the impact from unlabeled data while leveraging them to update the feature embedding.
Sparse Pose Trajectory Completion
In the absence of dense pose sampling in image space, these latent space trajectories provide cross-modal guidance for learning.
Breadcrumbs: Adversarial Class-Balanced Sampling for Long-tailed Recognition
It is shown that, unlike class-balanced sampling, this is an adversarial augmentation strategy.
GistNet: a Geometric Structure Transfer Network for Long-Tailed Recognition
A new learning algorithm is then proposed for GeometrIc Structure Transfer (GIST), with resort to a combination of loss functions that combine class-balanced and random sampling to guarantee that, while overfitting to the popular classes is restricted to geometric parameters, it is leveraged to transfer class geometry from popular to few-shot classes.
DP-Image: Differential Privacy for Image Data in Feature Space
The excessive use of images in social networks, government databases, and industrial applications has posed great privacy risks and raised serious concerns from the public.
IdentityDP: Differential Private Identification Protection for Face Images
We propose IdentityDP, a face anonymization framework that combines a data-driven deep neural network with a differential privacy (DP) mechanism.
Spurious Local Minima Are Common for Deep Neural Networks with Piecewise Linear Activations
A motivating example is given to explain the reason for the existence of spurious local minima: each output neuron of deep fully-connected networks and CNNs with piecewise linear activations produces a continuous piecewise linear (CPWL) output, and different pieces of CPWL output can fit disjoint groups of data samples when minimizing the empirical risk.
SLAKE: A Semantically-Labeled Knowledge-Enhanced Dataset for Medical Visual Question Answering
We show that SLAKE can be used to facilitate the development and evaluation of Med-VQA systems.
Firefly Neural Architecture Descent: a General Approach for Growing Neural Networks
We propose firefly neural architecture descent, a general framework for progressively and dynamically growing neural networks to jointly optimize the networks' parameters and architectures.
Discrete Knowledge Graph Embedding based on Discrete Optimization
This paper proposes a discrete knowledge graph (KG) embedding (DKGE) method, which projects KG entities and relations into the Hamming space based on a computationally tractable discrete optimization algorithm, to solve the formidable storage and computation cost challenges in traditional continuous graph embedding methods.
A Coach-Player Framework for Dynamic Team Composition
The performance of our method is comparable or even better than the setting where all players have a full view of the environment, but no coach.
Personalized and Invertible Face De-Identification by Disentangled Identity Information Manipulation
The popularization of intelligent devices including smartphones and surveillance cameras results in more serious privacy issues.
Deep Transport Network for Unsupervised Video Object Segmentation
The popular unsupervised video object segmentation methods fuse the RGB frame and optical flow via a two-stream network.
Ranked #3 on
Unsupervised Video Object Segmentation
on FBMS test
D-Unet: A Dual-encoder U-Net for Image Splicing Forgery Detection and Localization
The unfixed encoder autonomously learns the image fingerprints that differentiate between the tampered and non-tampered regions, whereas the fixed encoder intentionally provides the direction information that assists the learning and detection of the network.
Towards Playing Full MOBA Games with Deep Reinforcement Learning
However, existing work falls short in handling the raw game complexity caused by the explosion of agent combinations, i. e., lineups, when expanding the hero pool in case that OpenAI's Dota AI limits the play to a pool of only 17 heroes.
When Machine Learning Meets Privacy: A Survey and Outlook
The newly emerged machine learning (e. g. deep learning) methods have become a strong driving force to revolutionize a wide range of industries, such as smart healthcare, financial technology, and surveillance systems.
The SLT 2021 children speech recognition challenge: Open datasets, rules and baselines
Automatic speech recognition (ASR) has been significantly advanced with the use of deep learning and big data.
Sound Audio and Speech Processing
Machine versus Human Attention in Deep Reinforcement Learning Tasks
1) How similar are the visual representations learned by RL agents and humans when performing the same task?
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.
The NVIDIA PilotNet Experiments
Four years ago, an experimental system known as PilotNet became the first NVIDIA system to steer an autonomous car along a roadway.
Identifying Melanoma Images using EfficientNet Ensemble: Winning Solution to the SIIM-ISIC Melanoma Classification Challenge
We present our winning solution to the SIIM-ISIC Melanoma Classification Challenge.
Google Landmark Recognition 2020 Competition Third Place Solution
We present our third place solution to the Google Landmark Recognition 2020 competition.
Learning Spatio-Appearance Memory Network for High-Performance Visual Tracking
Existing visual object tracking usually learns a bounding-box based template to match the targets across frames, which cannot accurately learn a pixel-wise representation, thereby being limited in handling severe appearance variations.
Spin-wave focusing induced skyrmion generation
We propose a new method to generate magnetic skyrmions through spin-wave focusing in chiral ferromagnets. A lens is constructed to focus spin waves by a curved interface between two ferromagnetic thin films with different perpendicular magnetic anisotropies.
Mesoscale and Nanoscale Physics
Variance-Reduced Off-Policy Memory-Efficient Policy Search
To achieve variance-reduced off-policy-stable policy optimization, we propose an algorithm family that is memory-efficient, stochastically variance-reduced, and capable of learning from off-policy samples.
Zero-Shot Learning from Adversarial Feature Residual to Compact Visual Feature
In addition, considering that the visual features from categorization CNNs are generally inconsistent with their semantic features, a simple feature selection strategy is introduced for extracting more compact semantic visual features.
Oriented Object Detection in Aerial Images with Box Boundary-Aware Vectors
To address this issue, in this work we extend the horizontal keypoint-based object detector to the oriented object detection task.
Ranked #1 on
Object Detection
on DOTA
Meta-Learning with Network Pruning
To remedy this deficiency, we propose a network pruning based meta-learning approach for overfitting reduction via explicitly controlling the capacity of network.
RDP-GAN: A Rényi-Differential Privacy based Generative Adversarial Network
Generative adversarial network (GAN) has attracted increasing attention recently owing to its impressive ability to generate realistic samples with high privacy protection.
Understanding Global Loss Landscape of One-hidden-layer ReLU Networks, Part 2: Experiments and Analysis
The existence of local minima for one-hidden-layer ReLU networks has been investigated theoretically in [8].
Proximal Gradient Temporal Difference Learning: Stable Reinforcement Learning with Polynomial Sample Complexity
In this paper, we introduce proximal gradient temporal difference learning, which provides a principled way of designing and analyzing true stochastic gradient temporal difference learning algorithms.
Stable and Efficient Policy Evaluation
Policy evaluation algorithms are essential to reinforcement learning due to their ability to predict the performance of a policy.
Finite-Sample Analysis of Proximal Gradient TD Algorithms
In this paper, we analyze the convergence rate of the gradient temporal difference learning (GTD) family of algorithms.
Regularized Off-Policy TD-Learning
We present a novel $l_1$ regularized off-policy convergent TD-learning method (termed RO-TD), which is able to learn sparse representations of value functions with low computational complexity.
Fast Enhancement for Non-Uniform Illumination Images using Light-weight CNNs
This paper proposes a new light-weight convolutional neural network (5k parameters) for non-uniform illumination image enhancement to handle color, exposure, contrast, noise and artifacts, etc., simultaneously and effectively.
A Light-Weighted Convolutional Neural Network for Bitemporal SAR Image Change Detection
We verify our light-weighted neural network on four sets of bitemporal SAR images.
Few-Shot Open-Set Recognition using Meta-Learning
It is argued that the classic softmax classifier is a poor solution for open-set recognition, since it tends to overfit on the training classes.
A Convolutional Neural Network with Parallel Multi-Scale Spatial Pooling to Detect Temporal Changes in SAR Images
Furthermore, to verify the generalization of the proposed method, we apply our proposed method to the cross-dataset bitemporal SAR image change detection, where the MSSP network (MSSP-Net) is trained on a dataset and then applied to an unknown testing dataset.
Mean-Variance Policy Iteration for Risk-Averse Reinforcement Learning
We present a mean-variance policy iteration (MVPI) framework for risk-averse control in a discounted infinite horizon MDP optimizing the variance of a per-step reward random variable.
APPLD: Adaptive Planner Parameter Learning from Demonstration
Existing autonomous robot navigation systems allow robots to move from one point to another in a collision-free manner.
Exploit Clues from Views: Self-Supervised and Regularized Learning for Multiview Object Recognition
Multiview recognition has been well studied in the literature and achieves decent performance in object recognition and retrieval task.
Estimation of genome size using k-mer frequencies from corrected long reads
We show that corrected third-generation data can be used to count k-mer frequencies and estimate genome size reliably, in replacement of using second-generation data.
Adaptive Graph Convolutional Network with Attention Graph Clustering for Co-saliency Detection
Second, we develop an attention graph clustering algorithm to discriminate the common objects from all the salient foreground objects in an unsupervised fashion.
Dual Temporal Memory Network for Efficient Video Object Segmentation
We present an end-to-end network which stores short- and long-term video sequence information preceding the current frame as the temporal memories to address the temporal modeling in VOS.
One-shot visual object segmentation
Semantic Segmentation
+2
Some Geometrical and Topological Properties of DNNs' Decision Boundaries
Geometry and topology of decision regions are closely related with classification performance and robustness against adversarial attacks.
Understanding Global Loss Landscape of One-hidden-layer ReLU Networks, Part 1: Theory
For one-hidden-layer ReLU networks, we prove that all differentiable local minima are global inside differentiable regions.
GradientDICE: Rethinking Generalized Offline Estimation of Stationary Values
Namely, the optimization problem in GenDICE is not a convex-concave saddle-point problem once nonlinearity in optimization variable parameterization is introduced to ensure positivity, so any primal-dual algorithm is not guaranteed to converge or find the desired solution.
Video Saliency Prediction Using Enhanced Spatiotemporal Alignment Network
Due to a variety of motions across different frames, it is highly challenging to learn an effective spatiotemporal representation for accurate video saliency prediction (VSP).
Deep Object Co-segmentation via Spatial-Semantic Network Modulation
With the multi-resolution features of the relevant images as input, we design a spatial modulator to learn a mask for each image.
Robust Conditional GAN from Uncertainty-Aware Pairwise Comparisons
Conditional generative adversarial networks have shown exceptional generation performance over the past few years.
Object-Guided Instance Segmentation for Biological Images
Along with the instance normalization, the model is able to recover the target object distribution and suppress the distribution of neighboring attached objects.
Heterogeneous Deep Graph Infomax
The derived node representations can be used to serve various downstream tasks, such as node classification and node clustering.
Ranked #7 on
Heterogeneous Node Classification
on DBLP (PACT) 14k
FeCaffe: FPGA-enabled Caffe with OpenCL for Deep Learning Training and Inference on Intel Stratix 10
In this paper, we firstly propose the FeCaffe, i. e. FPGA-enabled Caffe, a hierarchical software and hardware design methodology based on the Caffe to enable FPGA to support mainline deep learning development features, e. g. training and inference with Caffe.
Provably Convergent Two-Timescale Off-Policy Actor-Critic with Function Approximation
With the help of the emphasis critic and the canonical value function critic, we show convergence for COF-PAC, where the critics are linear and the actor can be nonlinear.
DualSMC: Tunneling Differentiable Filtering and Planning under Continuous POMDPs
A major difficulty of solving continuous POMDPs is to infer the multi-modal distribution of the unobserved true states and to make the planning algorithm dependent on the perceived uncertainty.
Meta-Learning with Network Pruning for Overfitting Reduction
Meta-Learning has achieved great success in few-shot learning.
A Human-Centered Data-Driven Planner-Actor-Critic Architecture via Logic Programming
Recent successes of Reinforcement Learning (RL) allow an agent to learn policies that surpass human experts but suffers from being time-hungry and data-hungry.
Optimal Function Approximation with Relu Neural Networks
We consider in this paper the optimal approximations of convex univariate functions with feed-forward Relu neural networks.
Transfer Learning-Based Label Proportions Method with Data of Uncertainty
Learning with label proportions (LLP), which is a learning task that only provides unlabeled data in bags and each bag's label proportion, has widespread successful applications in practice.
Multi-scale Cell Instance Segmentation with Keypoint Graph based Bounding Boxes
In this paper, we propose a new box-based cell instance segmentation method.
A Joint Planning and Learning Framework for Human-Aided Decision-Making
Conventional reinforcement learning (RL) allows an agent to learn policies via environmental rewards only, with a long and slow learning curve, especially at the beginning stage.
Non-uniqueness phenomenon of object representation in modelling IT cortex by deep convolutional neural network (DCNN)
Recently DCNN (Deep Convolutional Neural Network) has been advocated as a general and promising modelling approach for neural object representation in primate inferotemporal cortex.
Towards Photo-Realistic Visible Watermark Removal with Conditional Generative Adversarial Networks
Visible watermark plays an important role in image copyright protection and the robustness of a visible watermark to an attack is shown to be essential.
Optimal Control of Complex Systems through Variational Inference with a Discrete Event Decision Process
However, such real-world complex system control is difficult to achieve because of high-dimensional and non-linear system dynamics, and the exploding state and action spaces for the decision maker.
Anonymized BERT: An Augmentation Approach to the Gendered Pronoun Resolution Challenge
We present our 7th place solution to the Gendered Pronoun Resolution challenge, which uses BERT without fine-tuning and a novel augmentation strategy designed for contextual embedding token-level tasks.
HR-TD: A Regularized TD Method to Avoid Over-Generalization
Temporal Difference learning with function approximation has been widely used recently and has led to several successful results.
Privacy Preserving Location Data Publishing: A Machine Learning Approach
By introducing a new formulation of the problem, we are able to apply machine learning algorithms for clustering the trajectories and propose to use $k$-means algorithm for this purpose.
Robust Matrix Completion State Estimation in Distribution Systems
It can estimate the system state in a low-observability system and has robust estimates without the bad data detection process in the face of multiple bad data.
Evolving the pulmonary nodules diagnosis from classical approaches to deep learning aided decision support: three decades development course and future prospect
Lung cancer is the commonest cause of cancer deaths worldwide, and its mortality can be reduced significantly by performing early diagnosis and screening.
Predicting pregnancy using large-scale data from a women's health tracking mobile application
Predicting pregnancy has been a fundamental problem in women's health for more than 50 years.
Sharpen Focus: Learning with Attention Separability and Consistency
Recent developments in gradient-based attention modeling have seen attention maps emerge as a powerful tool for interpreting convolutional neural networks.
QUOTA: The Quantile Option Architecture for Reinforcement Learning
In this paper, we propose the Quantile Option Architecture (QUOTA) for exploration based on recent advances in distributional reinforcement learning (RL).
Dantzig Selector with an Approximately Optimal Denoising Matrix and its Application to Reinforcement Learning
To make the problem computationally tractable, we propose a novel algorithm, termed as Optimal Denoising Dantzig Selector (ODDS), to approximately estimate the optimal denoising matrix.
SDRL: Interpretable and Data-efficient Deep Reinforcement Learning Leveraging Symbolic Planning
The three components cross-fertilize each other and eventually converge to an optimal symbolic plan along with the learned subtasks, bringing together the advantages of long-term planning capability with symbolic knowledge and end-to-end reinforcement learning directly from a high-dimensional sensory input.
A Block Coordinate Ascent Algorithm for Mean-Variance Optimization
Risk management in dynamic decision problems is a primary concern in many fields, including financial investment, autonomous driving, and healthcare.
Constrained-size Tensorflow Models for YouTube-8M Video Understanding Challenge
This paper presents our 7th place solution to the second YouTube-8M video understanding competition which challenges participates to build a constrained-size model to classify millions of YouTube videos into thousands of classes.
Privacy Preservation in Location-Based Services: A Novel Metric and Attack Model
In this paper, we incorporate a new type of side information based on consecutive location changes of users and propose a new metric called transition-entropy to investigate the location privacy preservation, followed by two algorithms to improve the transition-entropy for a given dummy generation algorithm.
PEORL: Integrating Symbolic Planning and Hierarchical Reinforcement Learning for Robust Decision-Making
Reinforcement learning and symbolic planning have both been used to build intelligent autonomous agents.
Satellite-relayed intercontinental quantum network
This was on the one hand the transmission of images in a one-time pad configuration from China to Austria as well as from Austria to China.
Quantum Physics
Feature Space Transfer for Data Augmentation
A new architecture, denoted the FeATure TransfEr Network (FATTEN), is proposed for the modeling of feature trajectories induced by variations of object pose.
O$^2$TD: (Near)-Optimal Off-Policy TD Learning
Temporal difference learning and Residual Gradient methods are the most widely used temporal difference based learning algorithms; however, it has been shown that none of their objective functions is optimal w. r. t approximating the true value function $V$.
Dual Iterative Hard Thresholding: From Non-convex Sparse Minimization to Non-smooth Concave Maximization
It remains open to explore duality theory and algorithms in such a non-convex and NP-hard problem setting.
Neural Clinical Paraphrase Generation with Attention
Paraphrase generation is important in various applications such as search, summarization, and question answering due to its ability to generate textual alternatives while keeping the overall meaning intact.
Regression-based Hypergraph Learning for Image Clustering and Classification
Moreover, we plug RH into two conventional hypergraph learning frameworks, namely hypergraph spectral clustering and hypergraph transduction, to present Regression-based Hypergraph Spectral Clustering (RHSC) and Regression-based Hypergraph Transduction (RHT) models for addressing the image clustering and classification issues.
Bayesian Model Adaptation for Crowd Counts
A large video dataset for the evaluation of adaptation approaches to crowd counting is also introduced.
Heterogeneous Metric Learning with Content-based Regularization for Software Artifact Retrieval
Specifically, this method can capture both the inherent information in the source codes and the semantic information hidden in the comments, descriptions, and identifiers of the source codes.
Understanding Zipf's law of word frequencies through sample-space collapse in sentence formation
The formation of sentences is a highly structured and history-dependent process.
Proximal Reinforcement Learning: A New Theory of Sequential Decision Making in Primal-Dual Spaces
In this paper, we set forth a new vision of reinforcement learning developed by us over the past few years, one that yields mathematically rigorous solutions to longstanding important questions that have remained unresolved: (i) how to design reliable, convergent, and robust reinforcement learning algorithms (ii) how to guarantee that reinforcement learning satisfies pre-specified "safety" guarantees, and remains in a stable region of the parameter space (iii) how to design "off-policy" temporal difference learning algorithms in a reliable and stable manner, and finally (iv) how to integrate the study of reinforcement learning into the rich theory of stochastic optimization.
3D Face Tracking and Multi-Scale, Spatio-temporal Analysis of Linguistically Significant Facial Expressions and Head Positions in ASL
Essential grammatical information is conveyed in signed languages by clusters of events involving facial expressions and movements of the head and upper body.
Bayesian Analysis for miRNA and mRNA Interactions Using Expression Data
Compared to the point estimate algorithms, which only provide single estimates for those parameters, the Bayesian methods are more meaningful and provide credible intervals, which take into account the uncertainty of the inferred interactions of the miRNA and mRNA.
Recognition of Nonmanual Markers in American Sign Language (ASL) Using Non-Parametric Adaptive 2D-3D Face Tracking
This paper addresses the problem of automatically recognizing linguistically significant nonmanual expressions in American Sign Language from video.
Basis Construction from Power Series Expansions of Value Functions
This paper explores links between basis construction methods in Markov decision processes and power series expansions of value functions.
