Author: Aneesh Tickoo

Aneesh Tickoo
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Aneesh Tickoo is a consulting intern at MarktechPost. He is currently pursuing his undergraduate degree in Data Science and Artificial Intelligence from the Indian Institute of Technology(IIT), Bhilai. He spends most of his time working on projects aimed at harnessing the power of machine learning. His research interest is image processing and is passionate about building solutions around it. He loves to connect with people and collaborate on interesting projects.

Meet CelebV-Text: A Large-Scale Facial Text-Video Dataset Comprising 70,000 in-the-wild Face Video Clips with Diverse Visual Content

AI Paper Summary
The disciplines of computer vision and computer graphics have recently paid a lot of attention to text-driven video production. Video material may be created...

This AI Paper Introduces a Novel Wavelet-Based Diffusion Framework that Demonstrates Superior Performance on both Image Fidelity and Sampling Speed

AI Paper Summary
Diffusion models have advanced significantly and attracted much study attention despite being recently presented. Such models reverse the diffusion process to produce clear, high-quality...

A New AI Research Introduces Cluster-Branch-Train-Merge (CBTM): A Simple But Effective Method For Scaling Expert Language Models With Unsupervised Domain Discovery

AI Paper Summary
Up to a trillion text tokens are used n the training of language models (LMs). This increases performance on several jobs but comes at...

A New AI Research From the University of Maryland Proposes a Model-Agnostic Secret-Keeping Approach in Question-Answering Systems

AI Paper Summary
Improved accuracy is the main goal of most Question Answering (QA) efforts. The goal has been to make the response supplied text as accessible...

Meet Instruct-NeRF2NeRF: An AI Method For Editing 3D Scenes With Text-Instructions

AI Paper Summary
It has never been simpler to capture a realistic digital representation of a real-world 3D scene, thanks to the development of effective neural 3D...

This AI Paper Proposes UPRISE: A Lightweight and Versatile Approach to Improve the Zero-Shot Performance of Different Large Language Models LLMs on Various Tasks

AI Paper Summary
Large language models like GPT-3, OPT, and BLOOM have demonstrated impressive capabilities in various applications. According to a recent study, there are two key...

Microsoft Researchers Propose A New AI Method That Uses Both Forward And Backward Language Models To Meet In The Middle And Improve The Training...

AI Paper Summary
Language models (LMs) have been extensively utilized for various aided writing activities, including text summarization, code completion, and paraphrasing. LMs are effective tools for...

Meet ViperGPT: A Python Framework that Combines Vision and Language Models Using Code Generation to Achieve State-of-the-Art Results

AI Paper Summary
The groundbreaking work of Neural Module Networks in prior years aimed to break down jobs into simpler modules. Through training from beginning to finish...

Meet Petals: An Open-Source Artificial Intelligence (AI) System That Can Run 100B+ Language Models At Home Bit-Torrent Style

AI Paper Summary
The NLP community has recently discovered that pretrained language models may accomplish various real-world activities with the help of minor adjustments or direct assistance....

Meet Magnushammer: A Transformer-based Approach to Premise Selection

AI Paper Summary
Artificial intelligence's main focus has been on automating mathematical reasoning. More recently, machine learning has greatly benefited both informal and formal theorem proving. The...

UT Austin Researchers Propose WICE: A New Dataset for Fact Verification Built on Real Claims in Wikipedia with Fine-Grained Annotations

AI Paper Summary
Natural language inference and textual entailment are enduring issues in NLP that can take many shapes. There are some significant gaps when current entailment...

This AI Paper Proposes CaFo: A Cascade of Foundation Models that Incorporates Diverse Prior Knowledge of Various Pre-Training Paradigms for Better Few-Shot Learning

AI Paper Summary
Many datasets, convolutional neural networks, and transformers have achieved remarkable success on various vision tasks. Instead, few-shot learning, where the networks are confined to...
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