Articles About Machine Learning

October 1, 2020 CONTRASTIVE LEARNING, Machine Learning

G-SimCLR: Self-Supervised Contrastive Learning with Guided Projection via Pseudo Labelling

In the realms of computer vision, it is evident that deep neural networks perform better in a supervised setting with a large amount of labeled data. The representations learned with supervision are not only of high quality but also helps the model in enhancing its accuracy… However, the collection and annotation of a large dataset are costly and time-consuming. To avoid the same, there has been a lot of research going on in the field of unsupervised visual representation learning […]

October 1, 2020 DIALOGUE UNDERSTANDING, Machine Learning

Utterance-level Dialogue Understanding: An Empirical Study

The recent abundance of conversational data on the Web and elsewhere calls for effective NLP systems for dialog understanding. Complete utterance-level understanding often requires context understanding, defined by nearby utterances… In recent years, a number of approaches have been proposed for various utterance-level dialogue understanding tasks. Most of these approaches account for the context for effective understanding. In this paper, we explore and quantify the role of context for different aspects of a dialogue, namely emotion, intent, and dialogue act […]

September 30, 2020 IMAGE SUPER-RESOLUTION, Machine Learning

Residual Feature Distillation Network for Lightweight Image Super-Resolution

Recent advances in single image super-resolution (SISR) explored the power of convolutional neural network (CNN) to achieve a better performance. Despite the great success of CNN-based methods, it is not easy to apply these methods to edge devices due to the requirement of heavy computation… To solve this problem, various fast and lightweight CNN models have been proposed. The information distillation network is one of the state-of-the-art methods, which adopts the channel splitting operation to extract distilled features. However, it […]

September 30, 2020 ADVERSARIAL ATTACK, Machine Learning

Improving Query Efficiency of Black-box Adversarial Attack

Deep neural networks (DNNs) have demonstrated excellent performance on various tasks, however they are under the risk of adversarial examples that can be easily generated when the target model is accessible to an attacker (white-box setting). As plenty of machine learning models have been deployed via online services that only provide query outputs from inaccessible models (e.g. Google Cloud Vision API2), black-box adversarial attacks (inaccessible target model) are of critical security concerns in practice rather than white-box ones… However, existing […]

September 30, 2020 CHATBOT, Machine Learning

Developing FB Chatbot Based on Deep Learning Using RASA Framework for University Enquiries

Smart systems for Universities powered by Artificial Intelligence have been massively developed to help humans in various tasks. The chatbot concept is not something new in today society which is developing with recent technology… College students or candidates of college students often need actual information like asking for something to customer service, especially during this pandemic, when it is difficult to have an immediate face-to-face meeting. Chatbots are functionally helping in several things such as curriculum information, admission for new […]

September 30, 2020 CONTRASTIVE LEARNING, Machine Learning

G-SimCLR : Self-Supervised Contrastive Learning with Guided Projection via Pseudo Labelling

September 30, 2020 CAUSAL INFERENCE, Machine Learning

Causal Intervention for Weakly-Supervised Semantic Segmentation

We present a causal inference framework to improve Weakly-Supervised Semantic Segmentation (WSSS). Specifically, we aim to generate better pixel-level pseudo-masks by using only image-level labels — the most crucial step in WSSS… We attribute the cause of the ambiguous boundaries of pseudo-masks to the confounding context, e.g., the correct image-level classification of “horse” and “person” may be not only due to the recognition of each instance, but also their co-occurrence context, making the model inspection (e.g., CAM) hard to distinguish […]

September 30, 2020 Machine Learning, PARAPHRASE GENERATION

iNLTK: Natural Language Toolkit for Indic Languages

We present iNLTK, an open-source NLP library consisting of pre-trained language models and out-of-the-box support for Paraphrase Generation, Textual Similarity, Sentence Embeddings, Word Embeddings, Tokenization and Text Generation in 13 Indic Languages. By using pre-trained models from iNLTK for text classification on publicly available datasets, we significantly outperform previously reported results… On these datasets, we also show that by using pre-trained models and paraphrases from iNLTK, we can achieve more than 95% of the previous best performance by using less […]

September 30, 2020 Machine Learning

Auto-Sklearn for Automated Machine Learning in Python

Last Updated on September 12, 2020 Automated Machine Learning (AutoML) refers to techniques for automatically discovering well-performing models for predictive modeling tasks with very little user involvement. Auto-Sklearn is an open-source library for performing AutoML in Python. It makes use of the popular Scikit-Learn machine learning library for data transforms and machine learning algorithms and uses a Bayesian Optimization search procedure to efficiently discover a top-performing model pipeline for a given dataset. In this tutorial, you will discover how to […]

September 29, 2020 Machine Learning, NETWORK PRUNING

A Gradient Flow Framework For Analyzing Network Pruning

Recent network pruning methods focus on pruning models early-on in training. To estimate the impact of removing a parameter, these methods use importance measures that were originally designed for pruning trained models… Despite lacking justification for their use early-on in training, models pruned using such measures result in surprisingly minimal accuracy loss. To better explain this behavior, we develop a general, gradient-flow based framework that relates state-of-the-art importance measures through an order of time-derivative of the norm of model parameters. […]

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