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.

University of Cambridge Researchers Introduce a Dataset of 50,000 Synthetic and Photorealistic Foot Images along with a Novel AI Library for Foot

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The health, fashion, and fitness industries are highly interested in the difficult computer vision problem of 3D reconstructing human body parts from pictures. They...

MIT Researchers Introduce LILO: A Neuro-Symbolic Framework for Learning Interpretable Libraries for Program Synthesis

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Big language models (LLMs) are becoming increasingly skilled in programming in various contexts, such as finishing partly written code, interacting with human programmers, and...

Peeking Inside Pandora’s Box: Unveiling the Hidden Complexities of Language Model Datasets with ‘What’s in My Big Data’? (WIMBD)

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Machine learning relies on data as its building block. New datasets are a key factor in research and the development of innovative models since...

Revolutionizing AI’s Listening Skills: Tsinghua University and ByteDance Unveil SALMONN – A Groundbreaking Multimodal Neural Network for Advanced Audio Processing

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In several natural language processing applications, text-based big language models have shown impressive and even human-level performance. In the meanwhile, an LLM training paradigm...

Researchers at Stanford Introduce RoboFuME: Revolutionizing Robotic Learning with Minimal Human Input

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In many domains that involve machine learning, a widely successful paradigm for learning task-specific models is to first pre-train a general-purpose model from an...

UC San Diego Researchers DYffusion: A Dynamics-informed Diffusion Model for Spatiotemporal Forecasting

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Projecting a dynamic system's future behaviour, or dynamics forecasting, entails understanding the underlying dynamics that drive the system's evolution to make precise predictions about...

This AI Research from Google Reveals How Encoding Graph Data Elevates Language Model Performance on Complex Tasks

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Large language model (LLM) research and applications have advanced remarkably in recent years. These generative models have enthralled the artificial intelligence community, and many...

This AI Research Introduces DreamCraft3D: A Hierarchical Approach for Creating 3D Material that Generates Cohesive and High-Fidelity 3D Models

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The incredible popularity of 2D generative modelling has significantly impacted how they produce visual material. Deep generative networks still have a great deal of...

Researchers from China Introduced a Novel Compression Paradigm called Retrieval-based Knowledge Transfer (RetriKT): Revolutionizing the Deployment of Large-Scale Pre-Trained Language Models in Real-World Applications

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Natural language processing (NLP) applications have shown remarkable performance using pre-trained language models (PLMs), including BERT/RoBERTa. However, because of their enormous complexity, these models—which...

Apple Researchers Introduce A Groundbreaking Artificial Intelligence Approach to Dense 3D Reconstruction from Dynamically-Posed RGB Images

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With learnt priors, RGB-only reconstruction with a monocular camera has made significant strides toward resolving the issues of low-texture areas and the inherent ambiguity...

Stanford and UT Austin Researchers Propose Contrastive Preference Learning (CPL): A Simple Reinforcement Learning RL-Free Method for RLHF that Works with Arbitrary MDPs and...

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The challenge of matching human preferences to big pretrained models has gained prominence in the study as these models have grown in performance. This...

Researchers from CMU and NYU Propose LLMTime: An Artificial Intelligence Method for Zero-Shot Time Series Forecasting with Large Language Models (LLMs)

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Despite having some parallels to other sequence modeling issues, like text, audio, or video, time series has two characteristics that make it particularly difficult....
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