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This work presents a residual learning framework to ease the training of networks that are substantially deeper than those used previously, and provides comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth.

The Segment Anything Model (SAM) is introduced: a new task, model, and dataset for image segmentation, and its zero-shot performance is impressive – often competitive with or even superior to prior fully supervised results.

This project aims to provide an efficient and robust real-time emotion identification framework that makes use of paralinguistic factors such as intensity, pitch, and MFCC and employs Diffusion Map to reduce data redundancy and high dimensionality.

This chapter presents techniques for statistical machine learning using Support Vector Machines (SVM) to recognize the patterns and classify them, predicting structured objects using SVM, k-nearest neighbor method for classification, and Naive Bayes classifiers.

The development of TRIPOD+AI is described and the expanded 27 item checklist with more detailed explanation of each reporting recommendation is presented, and the TRIPOD+AI for Abstracts checklist is presented.

An innovative convolutional neural network architecture aimed at addressing challenges of detection and classification of apple fruit diseases is proposed and experimentally validated, achieving a remarkable classification accuracy of 95.37%.

GPT-4, a large-scale, multimodal model which can accept image and text inputs and produce text outputs, is developed, a Transformer-based model pre-trained to predict the next token in a document which exhibits human-level performance on various professional and academic benchmarks.

This work introduces Flamingo, a family of Visual Language Models (VLM) with this ability to bridge powerful pretrained vision-only and language-only models, handle sequences of arbitrarily interleaved visual and textual data, and seamlessly ingest images or videos as inputs.

New methods in the UCSF ChimeraX molecular modeling package are described that take advantage of machine‐learning structure predictions, provide likelihood‐based fitting in maps, and compute per‐residue scores to identify modeling errors.

The most common evaluation metrics used for the typical supervised ML tasks including binary, multi-class, and multi-label classification, regression, image segmentation, object detection, and information retrieval are introduced.

Christoph Molnar. 2020. Interpretable Machine Learning: A Guide for Making Black Box Models Explainable, Lulu.com, pp. 318, ₹6690.

A new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely is proposed, which generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data.