Search Results for author:
Found 90 papers, 16 papers with code
A spatial compositional model (SCM) for linear unmixing and endmember uncertainty estimation
In this paper, we show that NCM can be used for calculating the uncertainty of the estimated endmembers with spatial priors incorporated for better unmixing.
Adaptive Multiple-Arm Identification
We study the problem of selecting $K$ arms with the highest expected rewards in a stochastic $n$-armed bandit game.
A Gaussian mixture model representation of endmember variability in hyperspectral unmixing
We show, given the GMM starting premise, that the distribution of the mixed pixel (under the linear mixing model) is also a GMM (and this is shown from two perspectives).
Unmixing urban hyperspectral imagery with a Gaussian mixture model on endmember variability
Spectral libraries were built by manually identifying and extracting pure spectra from both resolution images, resulting in 3, 287 spectra at 16 m and 15, 426 spectra at 4 m. We then unmixed ROIs of each resolution using the following unmixing algorithms: the set-based algorithms MESMA and AAM, and the distribution-based algorithms GMM, NCM, and BCM.
Hamiltonian Monte Carlo for Probabilistic Programs with Discontinuities
Hamiltonian Monte Carlo (HMC) is arguably the dominant statistical inference algorithm used in most popular "first-order differentiable" Probabilistic Programming Languages (PPLs).
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.
Tight Bounds for Collaborative PAC Learning via Multiplicative Weights
We study the collaborative PAC learning problem recently proposed in Blum et al.~\cite{BHPQ17}, in which we have $k$ players and they want to learn a target function collaboratively, such that the learned function approximates the target function well on all players' distributions simultaneously.
Channel Gating Neural Networks
Combining our method with knowledge distillation reduces the compute cost of ResNet-18 by 2. 6$\times$ without accuracy drop on ImageNet.
Inference Trees: Adaptive Inference with Exploration
We introduce inference trees (ITs), a new class of inference methods that build on ideas from Monte Carlo tree search to perform adaptive sampling in a manner that balances exploration with exploitation, ensures consistency, and alleviates pathologies in existing adaptive methods.
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.
Best Arm Identification in Linear Bandits with Linear Dimension Dependency
We study the best arm identification problem in linear bandits, where the mean reward of each arm depends linearly on an unknown $d$-dimensional parameter vector $\theta$, and the goal is to identify the arm with the largest expected reward.
Off-Policy Evaluation and Learning from Logged Bandit Feedback: Error Reduction via Surrogate Policy
Such an error reduction phenomenon is somewhat surprising as the estimated surrogate policy is less accurate than the given historical policy.
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.
On Exploration, Exploitation and Learning in Adaptive Importance Sampling
We study adaptive importance sampling (AIS) as an online learning problem and argue for the importance of the trade-off between exploration and exploitation in this adaptation.
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.
Efficient Interpretation of Deep Learning Models Using Graph Structure and Cooperative Game Theory: Application to ASD Biomarker Discovery
Cooperative game theory is advantageous here because it directly considers the interaction between features and can be applied to any machine learning method, making it a novel, more accurate way of determining instance-wise biomarker importance from deep learning models.
LF-PPL: A Low-Level First Order Probabilistic Programming Language for Non-Differentiable Models
We develop a new Low-level, First-order Probabilistic Programming Language (LF-PPL) suited for models containing a mix of continuous, discrete, and/or piecewise-continuous variables.
Nearly Minimax-Optimal Regret for Linearly Parameterized Bandits
We study the linear contextual bandit problem with finite action sets.
Collaborative Learning with Limited Interaction: Tight Bounds for Distributed Exploration in Multi-Armed Bandits
Best arm identification (or, pure exploration) in multi-armed bandits is a fundamental problem in machine learning.
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.
Thresholding Bandit with Optimal Aggregate Regret
We consider the thresholding bandit problem, whose goal is to find arms of mean rewards above a given threshold $\theta$, with a fixed budget of $T$ trials.
HGC: Hierarchical Group Convolution for Highly Efficient Neural Network
Using this operation, feature maps of different group cannot communicate, which restricts their representation capability.
Exploration via Hindsight Goal Generation
Goal-oriented reinforcement learning has recently been a practical framework for robotic manipulation tasks, in which an agent is required to reach a certain goal defined by a function on the state space.
Graph Neural Network for Interpreting Task-fMRI Biomarkers
Our pipeline can be generalized to other graph feature importance interpretation problems.
Dual-reference Age Synthesis
Age synthesis methods typically take a single image as input and use a specific number to control the age of the generated image.
$\sqrt{n}$-Regret for Learning in Markov Decision Processes with Function Approximation and Low Bellman Rank
Our learning algorithm, Adaptive Value-function Elimination (AVE), is inspired by the policy elimination algorithm proposed in (Jiang et al., 2017), known as OLIVE.
Divide, Conquer, and Combine: a New Inference Strategy for Probabilistic Programs with Stochastic Support
Universal probabilistic programming systems (PPSs) provide a powerful framework for specifying rich probabilistic models.
Comb Convolution for Efficient Convolutional Architecture
Convolutional neural networks (CNNs) are inherently suffering from massively redundant computation (FLOPs) due to the dense connection pattern between feature maps and convolution kernels.
Temporal Action Localization using Long Short-Term Dependency
In the present study, we developed a novel method, referred to as Gemini Network, for effective modeling of temporal structures and achieving high-performance temporal action localization.
Cross-Scale Residual Network for Multiple Tasks:Image Super-resolution, Denoising, and Deblocking
To this end, we propose the cross-scale residual network to exploit scale-related features and the inter-task correlations among the three tasks.
Furnishing Your Room by What You See: An End-to-End Furniture Set Retrieval Framework with Rich Annotated Benchmark Dataset
However, few works focus on the understanding of furniture within the scenes and a large-scale dataset is also lacked to advance the field.
Exploiting Operation Importance for Differentiable Neural Architecture Search
All the existing methods determine the importance of each operation directly by architecture weights.
Domain Adaptive Adversarial Learning Based on Physics Model Feedback for Underwater Image Enhancement
Owing to refraction, absorption, and scattering of light by suspended particles in water, raw underwater images suffer from low contrast, blurred details, and color distortion.
Anypath Routing Protocol Design via Q-Learning for Underwater Sensor Networks
As a promising technology in the Internet of Underwater Things, underwater sensor networks have drawn a widespread attention from both academia and industry.
Soft-Root-Sign Activation Function
At present, the most effective and widely-used activation function is ReLU.
Stochastically Differentiable Probabilistic Programs
Probabilistic programs with mixed support (both continuous and discrete latent random variables) commonly appear in many probabilistic programming systems (PPSs).
Collaborative Top Distribution Identifications with Limited Interaction
We give optimal time-round tradeoffs, as well as demonstrate complexity separations between top-$1$ arm identification and top-$m$ arm identifications for general $m$ and between fixed-time and fixed-confidence variants.
Almost Optimal Model-Free Reinforcement Learning via Reference-Advantage Decomposition
We study the reinforcement learning problem in the setting of finite-horizon episodic Markov Decision Processes (MDPs) with $S$ states, $A$ actions, and episode length $H$.
Adaptive Double-Exploration Tradeoff for Outlier Detection
We study a variant of the thresholding bandit problem (TBP) in the context of outlier detection, where the objective is to identify the outliers whose rewards are above a threshold.
Real-time Semantic Segmentation via Spatial-detail Guided Context Propagation
In SGCPNet, we propose the strategy of spatial-detail guided context propagation.
Model-Free Reinforcement Learning: from Clipped Pseudo-Regret to Sample Complexity
In this paper we consider the problem of learning an $\epsilon$-optimal policy for a discounted Markov Decision Process (MDP).
Linear Bandits with Limited Adaptivity and Learning Distributional Optimal Design
Motivated by practical needs such as large-scale learning, we study the impact of adaptivity constraints to linear contextual bandits, a central problem in online active learning.
Multinomial Logit Bandit with Low Switching Cost
We also present the ESUCB algorithm with item switching cost $O(N \log^2 T)$.
Graph Neural Network for Video Relocalization
In this paper, we focus on video relocalization task, which uses a query video clip as input to retrieve a semantic relative video clip in another untrimmed long video.
Pooling Regularized Graph Neural Network for fMRI Biomarker Analysis
We propose an interpretable GNN framework with a novel salient region selection mechanism to determine neurological brain biomarkers associated with disorders.
Generating Adjacency Matrix for Video Relocalization
In this paper, we continue our work on video relocalization task.
Efficient Competitive Self-Play Policy Optimization
Reinforcement learning from self-play has recently reported many successes.
Near-Optimal MNL Bandits Under Risk Criteria
We study MNL bandits, which is a variant of the traditional multi-armed bandit problem, under risk criteria.
Bayesian Policy Search for Stochastic Domains
In this work, we cast policy search in stochastic domains as a Bayesian inference problem and provide a scheme for encoding such problems as nested probabilistic programs.
Probabilistic Programs with Stochastic Conditioning
We tackle the problem of conditioning probabilistic programs on distributions of observable variables.
Symbolic Parallel Adaptive Importance Sampling for Probabilistic Program Analysis
Probabilistic software analysis aims at quantifying the probability of a target event occurring during the execution of a program processing uncertain incoming data or written itself using probabilistic programming constructs.
Unsupervised Self-training Algorithm Based on Deep Learning for Optical Aerial Images Change Detection
Then two set of pseudo labels are used to jointly train a student network with the same structure as the teacher.
Learning Guidance Rewards with Trajectory-space Smoothing
To make credit assignment easier, recent works have proposed algorithms to learn dense "guidance" rewards that could be used in place of the sparse or delayed environmental rewards.
Harnessing Distribution Ratio Estimators for Learning Agents with Quality and Diversity
Quality-Diversity (QD) is a concept from Neuroevolution with some intriguing applications to Reinforcement Learning.
Almost Optimal Model-Free Reinforcement Learningvia Reference-Advantage Decomposition
We study the reinforcement learning problem in the setting of finite-horizon1episodic Markov Decision Processes (MDPs) with S states, A actions, and episode length H. We propose a model-free algorithm UCB-ADVANTAGE and prove that it achieves \tilde{O}(\sqrt{H^2 SAT}) regret where T=KH and K is the number of episodes to play.
Logic Synthesis Meets Machine Learning: Trading Exactness for Generalization
If the function is incompletely-specified, the implementation has to be true only on the care set.
Adversarial Sample Enhanced Domain Adaptation: A Case Study on Predictive Modeling with Electronic Health Records
With the successful adoption of machine learning on electronic health records (EHRs), numerous computational models have been deployed to address a variety of clinical problems.
Few-shot Partial Multi-view Learning
The challenges of this task are twofold: (i) it is difficult to overcome the impact of data scarcity under the interference of missing views; (ii) the limited number of data exacerbates information scarcity, thus making it harder to address the view-missing issue in turn.
Off-Policy Reinforcement Learning with Delayed Rewards
We study deep reinforcement learning (RL) algorithms with delayed rewards.
Coordinate-wise Control Variates for Deep Policy Gradients
The control variates (CV) method is widely used in policy gradient estimation to reduce the variance of the gradient estimators in practice.
Few-shot Learning with Global Relatedness Decoupled-Distillation
To overcome these problems, we propose a new Global Relatedness Decoupled-Distillation (GRDD) method using the global category knowledge and the Relatedness Decoupled-Distillation (RDD) strategy.
SimCVD: Simple Contrastive Voxel-Wise Representation Distillation for Semi-Supervised Medical Image Segmentation
However, most existing learning-based approaches usually suffer from limited manually annotated medical data, which poses a major practical problem for accurate and robust medical image segmentation.
Almost Optimal Batch-Regret Tradeoff for Batch Linear Contextual Bandits
We study the optimal batch-regret tradeoff for batch linear contextual bandits.
Full-attention based Neural Architecture Search using Context Auto-regression
Thus, it is appropriate to consider using NAS methods to discover a better self-attention architecture automatically.
Fairness-aware Online Price Discrimination with Nonparametric Demand Models
To handle this general class, we propose a soft fairness constraint and develop a dynamic pricing policy that achieves $\tilde{O}(T^{4/5})$ regret.
Learning Long-Term Reward Redistribution via Randomized Return Decomposition
Based on this framework, this paper proposes a novel reward redistribution algorithm, randomized return decomposition (RRD), to learn a proxy reward function for episodic reinforcement learning.
A Survey on Scenario-Based Testing for Automated Driving Systems in High-Fidelity Simulation
To bridge this gap, in this work, we provide a generic formulation of scenario-based testing in high-fidelity simulation and conduct a literature review on the existing works.
Self-normalized Classification of Parkinson's Disease DaTscan Images
Classifying SPECT images requires a preprocessing step which normalizes the images using a normalization region.
Physical Backdoor Attacks to Lane Detection Systems in Autonomous Driving
However, DNN models are vulnerable to different types of adversarial attacks, which pose significant risks to the security and safety of the vehicles and passengers.
Imitation Learning from Observations under Transition Model Disparity
In this work, we propose an algorithm that trains an intermediary policy in the learner environment and uses it as a surrogate expert for the learner.
Dual Branch Neural Network for Sea Fog Detection in Geostationary Ocean Color Imager
The location of the sea fog in each image in SFDD is accurately marked.
Network Revenue Management with Demand Learning and Fair Resource-Consumption Balancing
We introduce the regularized revenue, i. e., the total revenue with a balancing regularization, as our objective to incorporate fair resource-consumption balancing into the revenue maximization goal.
AACC: Asymmetric Actor-Critic in Contextual Reinforcement Learning
Various methods, such as domain randomization, have been proposed to deal with such situations by training agents under different environmental setups, and therefore they can be generalized to different environments during deployment.
Near-Optimal Regret Bounds for Multi-batch Reinforcement Learning
Meanwhile, we show that to achieve $\tilde{O}(\mathrm{poly}(S, A, H)\sqrt{K})$ regret, the number of batches is at least $\Omega\left(H/\log_A(K)+ \log_2\log_2(K) \right)$, which matches our upper bound up to logarithmic terms.
Real-time speech enhancement with dynamic attention span
For real-time speech enhancement (SE) including noise suppression, dereverberation and acoustic echo cancellation, the time-variance of the audio signals becomes a severe challenge.
Medical visual question answering using joint self-supervised learning
Visual Question Answering (VQA) becomes one of the most active research problems in the medical imaging domain.
Time-Variance Aware Real-Time Speech Enhancement
Time-variant factors often occur in real-world full-duplex communication applications.
RAF: Holistic Compilation for Deep Learning Model Training
In this paper, we present RAF, a deep learning compiler for training.
High-Fidelity and Freely Controllable Talking Head Video Generation
Our method leverages both self-supervised learned landmarks and 3D face model-based landmarks to model the motion.
Advancing Incremental Few-shot Semantic Segmentation via Semantic-guided Relation Alignment and Adaptation
Incremental few-shot semantic segmentation (IFSS) aims to incrementally extend a semantic segmentation model to novel classes according to only a few pixel-level annotated data, while preserving its segmentation capability on previously learned base categories.
ABC-KD: Attention-Based-Compression Knowledge Distillation for Deep Learning-Based Noise Suppression
To begin with, we are among the first to comprehensively investigate mainstream KD techniques on DNS models to resolve the two challenges.
End-to-end Hyperspectral Image Change Detection Network Based on Band Selection
For hyperspectral image change detection (HSI-CD), one key challenge is to reduce band redundancy, as only a few bands are crucial for change detection while other bands may be adverse to it.
Adaptive Communications in Collaborative Perception with Domain Alignment for Autonomous Driving
To address these issues, we propose ACC-DA, a channel-aware collaborative perception framework to dynamically adjust the communication graph and minimize the average transmission delay while mitigating the side effects from the data heterogeneity.
Dual Feature Augmentation Network for Generalized Zero-shot Learning
To address these issues, we propose a novel Dual Feature Augmentation Network (DFAN), which comprises two feature augmentation modules, one for visual features and the other for semantic features.
Hybrid Reinforcement Learning for Optimizing Pump Sustainability in Real-World Water Distribution Networks
By leveraging operational data as a foundation for the agent's actions, we enhance the explainability of the agent's actions, foster more robust recommendations, and minimize error.
Prompt Pool based Class-Incremental Continual Learning for Dialog State Tracking
Inspired by the recently emerging prompt tuning method that performs well on dialog systems, we propose to use the prompt pool method, where we maintain a pool of key-value paired prompts and select prompts from the pool according to the distance between the dialog history and the prompt keys.
SmartCooper: Vehicular Collaborative Perception with Adaptive Fusion and Judger Mechanism
Moreover, in the context of collaborative perception, it is important to recognize that not all CAVs contribute valuable data, and some CAV data even have detrimental effects on collaborative perception.
Evaluating Decision Optimality of Autonomous Driving via Metamorphic Testing
In this paper, we focus on evaluating the decision-making quality of an ADS and propose the first method for detecting non-optimal decision scenarios (NoDSs), where the ADS does not compute optimal paths for AVs.
Controllable Relation Disentanglement for Few-Shot Class-Incremental Learning
In this paper, we propose to tackle Few-Shot Class-Incremental Learning (FSCIL) from a new perspective, i. e., relation disentanglement, which means enhancing FSCIL via disentangling spurious relation between categories.
Proof-of-Learning with Incentive Security
Most concurrent blockchain systems rely heavily on the Proof-of-Work (PoW) or Proof-of-Stake (PoS) mechanisms for decentralized consensus and security assurance.
