Paper reading list during my studies

Experiments in meta-learning visual attention in convolutional neural networks.

XCS224U - Winter 2021 - Syed/Aiswarya - Intent Classification - Few Shot - Code and Dataset

Comparitive analysis of different type of meta-learners

Higher-order gradients in PyTorch, Parallelized

Ben Gurion University "Deep Reinforcement Learning (372.2.5910)" course assignments & solutions

Engineering masters research project on multi-output neural processes

Batch-aware online task creation for meta-learning.

Domain Adaptation of Google's pre-trained Word Vectors to mid-19th century English Literature text | COL772 (NLP) @ IIT Delhi

Final Project from the course "Deep Learning" @ Data Science & Scientific Computing, University of Trieste, year 2020/2021, written in Python using PyTorch.

Implementation of Prototypical Networks for Few Shot Learning (https://arxiv.org/abs/1703.05175) in Pytorch

Meta-CapSL is a meta-learning model for predicting cancer-specific synthetic lethality (SL) as drug targets under low-data scenarios.

pytorch implementation of Optimization as a Model for Few-shot Learning

Dementia Prediction by Khalil El Asmar, Fatima Abu Salem, Hiyam Ghannam, Roaa Al-Feel

Implementation of data typology for imbalanced datasets.

Heterogeneous siamese neural network for bioactivity prediction using novel bioactivity representation

Reproducible material for Meta-Processing: A robust framework for multi-tasks seismic processing

Attention-based models such as BERT have produced state-of-the-art performance on many NLP tasks. However, these models suffer from catastrophic forgetting (CF) in a continual setting. We hypothesize that utilizing meta-learning methodologies such as OML to further train BERT will help learn contextual representations that are more robust to CF.

An implementation of the paper learning to learn