Search Results for author:
Found 17 papers, 5 papers with code
Contrastive Clustering
In this paper, we propose a one-stage online clustering method called Contrastive Clustering (CC) which explicitly performs the instance- and cluster-level contrastive learning.
Ranked #4 on
Image Clustering
on STL-10
(using extra training data)
Twin Contrastive Learning for Online Clustering
Specifically, we find that when the data is projected into a feature space with a dimensionality of the target cluster number, the rows and columns of its feature matrix correspond to the instance and cluster representation, respectively.
Ranked #1 on
Short Text Clustering
on Biomedical
Partially View-aligned Representation Learning with Noise-robust Contrastive Loss
To solve such a less-touched problem without the help of labels, we propose simultaneously learning representation and aligning data using a noise-robust contrastive loss.
UVMBench: A Comprehensive Benchmark Suite for Researching Unified Virtual Memory in GPUs
The recent introduction of Unified Virtual Memory (UVM) in GPUs offers a new programming model that allows GPUs and CPUs to share the same virtual memory space, shifts the complex memory management from programmers to GPU driver/ hardware, and enables kernel execution even when memory is oversubscribed.
Hardware Architecture
Deep Fair Clustering via Maximizing and Minimizing Mutual Information: Theory, Algorithm and Metric
Fair clustering aims to divide data into distinct clusters while preventing sensitive attributes (\textit{e. g.}, gender, race, RNA sequencing technique) from dominating the clustering.
Ranked #1 on
Image Clustering
on HAR
Horseshoe Regularization for Machine Learning in Complex and Deep Models
We also outline the recent computational developments in horseshoe shrinkage for complex models along with a list of available software implementations that allows one to venture out beyond the comfort zone of the canonical linear regression problems.
Large Scale Many-Objective Optimization Driven by Distributional Adversarial Networks
In this paper, we will propose a novel algorithm based on RVEA[1] framework and using Distributional Adversarial Networks (DAN) [2]to generate new offspring.
Many-Objective Estimation of Distribution Optimization Algorithm Based on WGAN-GP
EDA establishes a probability model to describe the distribution of solution from the perspective of population macroscopically by statistical learning method, and then randomly samples the probability model to generate a new population.
Surgical Phase Recognition in Laparoscopic Cholecystectomy
Automatic recognition of surgical phases in surgical videos is a fundamental task in surgical workflow analysis.
Incomplete Multi-view Clustering via Prototype-based Imputation
Thanks to our dual-stream model, both cluster- and view-specific information could be captured, and thus the instance commonality and view versatility could be preserved to facilitate IMvC.
Low-Switching Policy Gradient with Exploration via Online Sensitivity Sampling
We show that, our algorithm obtains an $\varepsilon$-optimal policy with only $\widetilde{O}(\frac{\text{poly}(d)}{\varepsilon^3})$ samples, where $\varepsilon$ is the suboptimality gap and $d$ is a complexity measure of the function class approximating the policy.
On the Model-Misspecification in Reinforcement Learning
However, in the face of model misspecification (a disparity between the ground-truth and optimal function approximators), it is shown that policy-based approaches can be robust even when the policy function approximation is under a large locally-bounded misspecification error, with which the function class may exhibit a $\Omega(1)$ approximation error in specific states and actions, but remains small on average within a policy-induced state distribution.
Automated Assessment of Critical View of Safety in Laparoscopic Cholecystectomy
Compared with classical open cholecystectomy, laparoscopic cholecystectomy (LC) is associated with significantly shorter recovery period, and hence is the preferred method.
Image Clustering with External Guidance
The core of clustering is incorporating prior knowledge to construct supervision signals.
Achieving Near-Optimal Regret for Bandit Algorithms with Uniform Last-Iterate Guarantee
This paper introduces a stronger performance measure, the uniform last-iterate (ULI) guarantee, capturing both cumulative and instantaneous performance of bandit algorithms.
Prognostic Covariate Adjustment for Logistic Regression in Randomized Controlled Trials
We demonstrate that prognostic score adjustment in logistic regression increases the power of the Wald test for the conditional odds ratio under a fixed sample size, or alternatively reduces the necessary sample size to achieve a desired power, compared to the unadjusted analysis.
An Empirical Study of Parameter Efficient Fine-tuning on Vision-Language Pre-train Model
A natural expectation for PEFTs is that the performance of various PEFTs is positively related to the data size and fine-tunable parameter size.
