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08/21/2023
BackTrack: Robust template update via Backward Tracking of candidate template
Variations of target appearance such as deformations, illumination varia...
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06/03/2023
SGEM: Test-Time Adaptation for Automatic Speech Recognition via Sequential-Level Generalized Entropy Minimization
Automatic speech recognition (ASR) models are frequently exposed to data...
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05/23/2023
ZET-Speech: Zero-shot adaptive Emotion-controllable Text-to-Speech Synthesis with Diffusion and Style-based Models
Emotional Text-To-Speech (TTS) is an important task in the development o...
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02/26/2023
Learning Input-agnostic Manipulation Directions in StyleGAN with Text Guidance
With the advantages of fast inference and human-friendly flexible manipu...
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12/06/2022
Diffusion Video Autoencoders: Toward Temporally Consistent Face Video Editing via Disentangled Video Encoding
Inspired by the impressive performance of recent face image editing meth...
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07/07/2022
Towards the Practical Utility of Federated Learning in the Medical Domain
Federated learning (FL) is an active area of research. One of the most s...
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06/26/2022
TAM: Topology-Aware Margin Loss for Class-Imbalanced Node Classification
Learning unbiased node representations under class-imbalanced graph data...
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12/16/2021
Saliency Grafting: Innocuous Attribution-Guided Mixup with Calibrated Label Mixing
The Mixup scheme suggests mixing a pair of samples to create an augmente...
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12/02/2021
Fighting Fire with Fire: Contrastive Debiasing without Bias-free Data via Generative Bias-transformation
Despite their remarkable ability to generalize with over-capacity networ...
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11/10/2021
Graph Transplant: Node Saliency-Guided Graph Mixup with Local Structure Preservation
Graph-structured datasets usually have irregular graph sizes and connect...
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10/06/2021
Online Hyperparameter Meta-Learning with Hypergradient Distillation
Many gradient-based meta-learning methods assume a set of parameters tha...
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09/17/2021
Distilling Linguistic Context for Language Model Compression
A computationally expensive and memory intensive neural network lies beh...
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09/05/2021
Cluster-Promoting Quantization with Bit-Drop for Minimizing Network Quantization Loss
Network quantization, which aims to reduce the bit-lengths of the networ...
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07/01/2021
FedMix: Approximation of Mixup under Mean Augmented Federated Learning
Federated learning (FL) allows edge devices to collectively learn a mode...
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06/06/2021
Meta-StyleSpeech : Multi-Speaker Adaptive Text-to-Speech Generation
With rapid progress in neural text-to-speech (TTS) models, personalized ...
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06/02/2021
Online Coreset Selection for Rehearsal-based Continual Learning
A dataset is a shred of crucial evidence to describe a task. However, ea...
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05/03/2021
RetCL: A Selection-based Approach for Retrosynthesis via Contrastive Learning
Retrosynthesis, of which the goal is to find a set of reactants for synt...
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03/26/2021
Mutually-Constrained Monotonic Multihead Attention for Online ASR
Despite the feature of real-time decoding, Monotonic Multihead Attention...
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02/07/2021
Model-Augmented Q-learning
In recent years, Q-learning has become indispensable for model-free rein...
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10/28/2020
Attribution Preservation in Network Compression for Reliable Network Interpretation
Neural networks embedded in safety-sensitive applications such as self-d...
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08/07/2020
Bootstrapping Neural Processes
Unlike in the traditional statistical modeling for which a user typicall...
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08/07/2020
Neural Complexity Measures
While various complexity measures for diverse model classes have been pr...
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07/23/2020
Few-shot Visual Reasoning with Meta-analogical Contrastive Learning
While humans can solve a visual puzzle that requires logical reasoning b...
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07/22/2020
Time-Reversal Symmetric ODE Network
Time-reversal symmetry, which requires that the dynamics of a system sho...
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07/17/2020
Distribution Aligning Refinery of Pseudo-label for Imbalanced Semi-supervised Learning
While semi-supervised learning (SSL) has proven to be a promising way fo...
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07/15/2020
A General Family of Stochastic Proximal Gradient Methods for Deep Learning
We study the training of regularized neural networks where the regulariz...
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07/14/2020
Learning to Sample with Local and Global Contexts in Experience Replay Buffer
Experience replay, which enables the agents to remember and reuse experi...
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07/02/2020
A Revision of Neural Tangent Kernel-based Approaches for Neural Networks
Recent theoretical works based on the neural tangent kernel (NTK) have s...
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07/02/2020
Compressed Sensing via Measurement-Conditional Generative Models
A pre-trained generator has been frequently adopted in compressed sensin...
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06/25/2020
Stochastic Subset Selection
Current machine learning algorithms are designed to work with huge volum...
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06/23/2020
Clinical Risk Prediction with Temporal Probabilistic Asymmetric Multi-Task Learning
Although recent multi-task learning methods have shown to be effective i...
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06/22/2020
Rapid Structural Pruning of Neural Networks with Set-based Task-Adaptive Meta-Pruning
As deep neural networks are growing in size and being increasingly deplo...
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06/22/2020
Federated Semi-Supervised Learning with Inter-Client Consistency
While existing federated learning approaches mostly require that clients...
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06/09/2020
Cost-effective Interactive Attention Learning with Neural Attention Processes
We propose a novel interactive learning framework which we refer to as I...
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03/06/2020
Federated Continual Learning with Adaptive Parameter Communication
There has been a surge of interest in continual learning and federated l...
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11/29/2019
Semi-Relaxed Quantization with DropBits: Training Low-Bit Neural Networks via Bit-wise Regularization
Neural Network quantization, which aims to reduce bit-lengths of the net...
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06/07/2019
Reliable Estimation of Individual Treatment Effect with Causal Information Bottleneck
Estimating individual level treatment effects (ITE) from observational d...
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06/01/2019
Sparsity Normalization: Stabilizing the Expected Outputs of Deep Networks
The learning of deep models, in which a numerous of parameters are super...
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05/30/2019
Learning to Balance: Bayesian Meta-Learning for Imbalanced and Out-of-distribution Tasks
While tasks could come with varying number of instances in realistic set...
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05/30/2019
Meta Dropout: Learning to Perturb Features for Generalization
A machine learning model that generalizes well should obtain low errors ...
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05/26/2019
Stochastic Gradient Methods with Block Diagonal Matrix Adaptation
Adaptive gradient approaches that automatically adjust the learning rate...
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05/14/2019
Spectral Approximate Inference
Given a graphical model (GM), computing its partition function is the mo...
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02/25/2019
ORACLE: Order Robust Adaptive Continual LEarning
The order of the tasks a continual learning model encounters may have la...
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06/05/2018
Mixed Effect Composite RNN-GP: A Personalized and Reliable Prediction Model for Healthcare
We present a personalized and reliable prediction model for healthcare, ...
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05/28/2018
Adaptive Network Sparsification via Dependent Variational Beta-Bernoulli Dropout
While variational dropout approaches have been shown to be effective for...
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05/24/2018
Uncertainty-Aware Attention for Reliable Interpretation and Prediction
Attention mechanism is effective in both focusing the deep learning mode...
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05/19/2018
M-estimation with the Trimmed l1 Penalty
We study high-dimensional M-estimators with the trimmed ℓ_1 penalty. Whi...
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12/21/2017
DropMax: Adaptive Stochastic Softmax
We propose DropMax, a stochastic version of softmax classifier which at ...
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10/04/2017
Multitask Learning using Task Clustering with Applications to Predictive Modeling and GWAS of Plant Varieties
Inferring predictive maps between multiple input and multiple output var...
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09/18/2017