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Found 10 papers, 1 papers with code
Reinforcement Learning for Optimizing RAG for Domain Chatbots
As a policy model, we experimented with a public gpt-2 model and an in-house BERT model.
Study of Encoder-Decoder Architectures for Code-Mix Search Query Translation
We propose a transformer-based approach for code-mix query translation to enable users to search with these queries.
Vernacular Search Query Translation with Unsupervised Domain Adaptation
For demonstration, we show results for Hindi to English query translation and use mBART-large-50 model as the baseline to improve upon.
Soft Attention Convolutional Neural Networks for Rare Event Detection in Sequences
In this work, we propose a Soft-Attention Convolutional Neural Network (CNN) based approach for rare event detection in sequences.
Deep Q learning for fooling neural networks
In this paper, we propose a Reinforcement Learning (RL) based approach to generate adversarial examples for the pre-trained (target) models.
Siamese networks for generating adversarial examples
We assume that the adversary do not possess any knowledge of the target data distribution, and we use an unlabeled mismatched dataset to query the target, e. g., for the ResNet-50 target, we use the Food-101 dataset as the query.
Knowledge distillation using unlabeled mismatched images
Current approaches for Knowledge Distillation (KD) either directly use training data or sample from the training data distribution.
Layer-wise training of deep networks using kernel similarity
The hierarchical feature representation built by deep networks enable compact and precise encoding of the data.
Ashwin: Plug-and-Play System for Machine-Human Image Annotation
We present an end-to-end machine-human image annotation system where each component can be attached in a plug-and-play fashion.
