Author: Mohammad Asjad

Mohammad Asjad
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Asjad is an intern consultant at Marktechpost. He is persuing B.Tech in mechanical engineering at the Indian Institute of Technology, Kharagpur. Asjad is a Machine learning and deep learning enthusiast who is always researching the applications of machine learning in healthcare.

Multi-Scale Neural Audio Codec (SNAC): An Wxtension of Residual Vector Quantization that Uses Quantizers Operating at Multiple Temporal Resolutions

AI Paper Summary
Neural audio compression has emerged as a critical challenge in digital signal processing, particularly in achieving efficient audio representation while preserving quality. Traditional audio...

Scaling Diffusion transformers (DiT): An AI Framework for Optimizing Text-to-Image Models Across Compute Budgets

AI Paper Summary
Large language models (LLMs) have demonstrated consistent scaling laws, revealing a power-law relationship between pretraining performance and computational resources. This relationship, expressed as C...

Model Kinship: The Degree of Similarity or Relatedness between LLMs, Analogous to Biological Evolution

AI Paper Summary
Large Language Models (LLMs) have gained significant traction in recent years, with fine-tuning pre-trained models for specific tasks becoming a common practice. However, this...

AutoDAN-Turbo: A Black-Box Jailbreak Method for LLMs with a Lifelong Agent

AI Paper Summary
Large language models (LLMs) have gained widespread adoption due to their advanced text understanding and generation capabilities. However, ensuring their responsible behavior through safety...

Stochastic Prompt Construction for Effective In-Context Reinforcement Learning in Large Language Models

AI Paper Summary
Large language models (LLMs) have demonstrated impressive capabilities in in-context learning (ICL), a form of supervised learning that doesn't require parameter updates. However, researchers...

UNC Chapel Hill Researchers Propose DataEnvGym: A Testbed of Teacher Environments for Data Generation Agents

AI Paper Summary
Large Language Models (LLMs) have gained significant attention in recent years, but improving their performance remains a challenging task. Researchers are striving to enhance...

ScienceAgentBench: A Rigorous AI Evaluation Framework for Language Agents in Scientific Discovery

AI Paper Summary
Large language models (LLMs) have emerged as powerful tools capable of performing complex tasks beyond text generation, including reasoning, tool learning, and code generation....

SQ-LLaVA: A New Visual Instruction Tuning Method that Enhances General-Purpose Vision-Language Understanding and Image-Oriented Question Answering through Visual Self-Questioning

AI Paper Summary
Large vision-language models have emerged as powerful tools for multimodal understanding, demonstrating impressive capabilities in interpreting and generating content that combines visual and textual...

From Prediction to Reasoning: Evaluating o1’s Impact on LLM Probabilistic Biases

AI Paper Summary
Large language models (LLMs) have gained significant attention in recent years, but understanding their capabilities and limitations remains a challenge. Researchers are trying to...

NVIDIA AI Releases OpenMathInstruct-2: A Math Instruction Tuning Dataset with 14M Problem-Solution Pairs Generated Using the Llama3.1-405B-Instruct Model

AI Paper Summary
Language models have made significant strides in mathematical reasoning, with synthetic data playing a crucial role in their development. However, the field faces significant...

Exploring In-Context Reinforcement Learning in LLMs with Sparse Autoencoders

AI Paper Summary
Large language models (LLMs) have demonstrated remarkable in-context learning capabilities across various domains, including translation, function learning, and reinforcement learning. However, the underlying mechanisms...

AI-Assisted Causal Inference: Using LLMs to Revolutionize Instrumental Variable Selection

AI Shorts
Endogeneity presents a significant challenge in conducting causal inference in observational settings. Researchers in social sciences, statistics, and related fields have developed various identification...
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