Search Results for author:
Found 17 papers, 9 papers with code
Can We Utilize Pre-trained Language Models within Causal Discovery Algorithms?
Scaling laws have allowed Pre-trained Language Models (PLMs) into the field of causal reasoning.
Threshold-aware Learning to Generate Feasible Solutions for Mixed Integer Programs
Our study investigates that a specific range of variable assignment rates (coverage) yields high-quality feasible solutions, where we suggest optimizing the coverage bridges the gap between the learning and MIP objectives.
Bag of Tricks for In-Distribution Calibration of Pretrained Transformers
We find that the ensemble model overfitted to the training set shows sub-par calibration performance and also observe that PLMs trained with confidence penalty loss have a trade-off between calibration and accuracy.
A Deep Reinforcement Learning Approach for Solving the Traveling Salesman Problem with Drone
Reinforcement learning has recently shown promise in learning quality solutions in many combinatorial optimization problems.
An Attention-LSTM Hybrid Model for the Coordinated Routing of Multiple Vehicles
State-less attention-based decoder fails to make such coordination between vehicles.
Neural Bootstrapping Attention for Neural Processes
Recently, Bootstrapping (Attentive) Neural Processes (B(A)NP) propose a bootstrap method to capture the functional uncertainty which can replace the latent variable in (Attentive) Neural Processes ((A)NP), thus overcoming the limitations of Gaussian assumption on the latent variable.
Optimal Algorithms for Stochastic Multi-Armed Bandits with Heavy Tailed Rewards
In simulation, the proposed estimator shows favorable performance compared to existing robust estimators for various $p$ values and, for MAB problems, the proposed perturbation strategy outperforms existing exploration methods.
Neural Bootstrapper
Bootstrapping has been a primary tool for ensemble and uncertainty quantification in machine learning and statistics.
AutoCLINT: The Winning Method in AutoCV Challenge 2019
NeurIPS 2019 AutoDL challenge is a series of six automated machine learning competitions.
torchgpipe: On-the-fly Pipeline Parallelism for Training Giant Models
We design and implement a ready-to-use library in PyTorch for performing micro-batch pipeline parallelism with checkpointing proposed by GPipe (Huang et al., 2019).
Scalable Neural Architecture Search for 3D Medical Image Segmentation
In this paper, a neural architecture search (NAS) framework is proposed for 3D medical image segmentation, to automatically optimize a neural architecture from a large design space.
Fast AutoAugment
Data augmentation is an essential technique for improving generalization ability of deep learning models.
Ranked #3 on
Image Classification
on SVHN
Tsallis Reinforcement Learning: A Unified Framework for Maximum Entropy Reinforcement Learning
By controlling the entropic index, we can generate various types of entropy, including the SG entropy, and a different entropy results in a different class of the optimal policy in Tsallis MDPs.
ChoiceNet: Robust Learning by Revealing Output Correlations
To this end, we present a novel framework referred to here as ChoiceNet that can robustly infer the target distribution in the presence of inconsistent data.
Task Agnostic Robust Learning on Corrupt Outputs by Correlation-Guided Mixture Density Networks
In this paper, we focus on weakly supervised learning with noisy training data for both classification and regression problems. We assume that the training outputs are collected from a mixture of a target and correlated noise distributions. Our proposed method simultaneously estimates the target distribution and the quality of each data which is defined as the correlation between the target and data generating distributions. The cornerstone of the proposed method is a Cholesky Block that enables modeling dependencies among mixture distributions in a differentiable manner where we maintain the distribution over the network weights. We first provide illustrative examples in both regression and classification tasks to show the effectiveness of the proposed method. Then, the proposed method is extensively evaluated in a number of experiments where we show that it constantly shows comparable or superior performances compared to existing baseline methods in the handling of noisy data.
Neural Stain-Style Transfer Learning using GAN for Histopathological Images
Consequently our model does not only transfers initial stain-styles to the desired one but also prevent the degradation of tumor classifier on transferred images.
Uncertainty-Aware Learning from Demonstration using Mixture Density Networks with Sampling-Free Variance Modeling
The proposed uncertainty-aware learning from demonstration method outperforms other compared methods in terms of safety using a complex real-world driving dataset.
