Ramnath Kumar – PhD Student, UCLA

Books

Posts

How to write a great research paper (August 12, 2022)
Learning to Teach with Dynamic Loss Functions (May 30, 2022)
PolyLoss-A polynomial expansion perspective of classification loss functions (April 15, 2022)
Entropy SGD-Biasing Gradient Descent into Wide Valleys (March 15, 2022)
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments (February 28, 2022)
MARL (February 14, 2022)
The Emergence of Individuality (January 15, 2022)
Towards Causal VQA (January 2, 2022)
How to train your MAML to excel in few-shot classification (December 1, 2021)
Task-Agnostic Meta-Learning for few-shot learning (November 15, 2021)
Diversity is all you need - Learning Skills without a Reward Function (October 29, 2021)
Learning an Embedding Space for Transferable Robot Skills (October 25, 2021)
The Multi-Armed Bandit Problem and Its Solutions (October 14, 2021)
Reinforcement Learning (October 13, 2021)
Meta Reinforcement Learning (October 9, 2021)
Invariance Principle Meets Information Bottleneck for Out-of-Distribution Generalization (August 26, 2021)
Adaptive Task Sampling for Meta Learning (June 18, 2021)
Meta-Learning for Batch Mode Active Learning (June 7, 2021)
Unsupervised Learning via Meta-Learning (May 28, 2021)
Curriculum in Gradient-Based Meta-Reinforcement Learning (May 14, 2021)
Barlow Twins- Self-Supervised Learning via Redundancy Reduction (May 9, 2021)
Fixing the train-test resolution discrepancy (May 1, 2021)
The effects of negative adaptation in Model-Agnostic Meta-Learning (April 3, 2021)
Neural Network Attributions- A causal Perspective (April 2, 2021)
How to represent part-whole hierarchies in neural network (April 1, 2021)
Meta-Reinforcement-Learning (March 26, 2021)
Meta-Learning-Learning to Learn Fast (March 25, 2021)
Learning representations by mutual information estimation and maximization (March 25, 2021)
One-Shot Free-View Neural Talking-head Synthesis for Video Conferencing (March 23, 2021)
Representation Learning with Contrastive Predictive Coding (March 22, 2021)
Learning models are few-shot learners (March 19, 2021)
A simple Framework for Contrastive Learning of Visual Representations (March 18, 2021)
Self training for few-shot transfer across extreme task differences (March 15, 2021)
Do Deeper Convolutional Networks Perform Better? (February 22, 2021)
Transductive Information Maximization For few-shot learning (December 17, 2020)
Combining label propagation and simple models out-performs graph neural networks (December 12, 2020)
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks (November 26, 2020)
La-MAML- Look-ahead Meta-Learning for Continual Learning (November 26, 2020)
Variational Graph Auto-Encoders (November 23, 2020)
Semi-supervised classification with graph convolutional networks (November 22, 2020)
Visualizing data using t-SNE (November 17, 2020)
Imagenet classification with deep convolutional neural networks (August 21, 2020)
Human-level concept learning through probabilistic program induction (August 18, 2020)
Reducing the dimensionality of data with neural networks (August 16, 2020)
Learning Representations by back-propagating errors (August 10, 2020)
On the variance of the adaptive learning rate and beyond (August 6, 2020)
An Adversarial Approach to Hard Triplet Generation (August 3, 2020)
Training Deep Networks with Stochastic Gradient Normalized by Layerwise Adaptive Second Moments (July 30, 2020)
AMC-AutoML for Model Compression and Acceleration of Mobile Devices (July 30, 2020)
Fooling automated surveillance cameras- Adversarial patches to attack person detection (July 24, 2020)
Large-Scale Study of Curiosity-Driven Learning (July 20, 2020)
Siamese Neural Networks for One-shot Image Recognition (July 19, 2020)
Deep Learning (July 18, 2020)
A Neural Algorithm of Artistic Style (July 17, 2020)
Dropout- A Simple Way to Prevent Neural Networks from Overfitting (July 16, 2020)
Understanding the difficulty of training deep feedforward neural networks (July 12, 2020)
Understanding Deep Learning requires rethinking generalization (July 12, 2020)
Efficient Estimation of Word Representations in Vector Space (July 12, 2020)
CartoonGAN- Generative Adversarial Networks for Photo Cartoonization (July 10, 2020)
Rich feature hierarchies for accurate object detection and semantic segmentation (July 3, 2020)
Revisiting Training Strategies and Generalization Performance in Deep Metric Learning (July 2, 2020)
A few useful things to know about Machine Learning (June 29, 2020)
Identifying and attacking the saddle point problem in high-dimensional non-convex optimization (June 26, 2020)
Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation (June 22, 2020)
Small-GAN- Speeding up GAN training using core-sets (June 21, 2020)
Generative Adversarial Networks (June 21, 2020)
Learning Individual Causal Effects from Networked Observational Data (May 29, 2020)