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.

Pixel Transformer: Challenging Locality Bias in Vision Models

AI Shorts
The deep learning revolution in computer vision has shifted from manually crafted features to data-driven approaches, highlighting the potential of reducing feature biases. This...

Neural Algorithmic Reasoning for Transformers: The TransNAR Framework

AI Paper Summary
Graph neural networks (GNNs), referred to as neural algorithmic reasoners (NARs), have shown effectiveness in robustly solving algorithmic tasks of varying input sizes, both...

Microsoft Researchers Introduce Samba 3.8B: A Simple Mamba+Sliding Window Attention Architecture that Outperforms Phi3-mini on Major Benchmarks

AI Shorts
Large Language Models (LLMs) face challenges in capturing complex long-term dependencies and achieving efficient parallelization for large-scale training. Attention-based models have dominated LLM architectures...

Enhancing Trust in Large Language Models: Fine-Tuning for Calibrated Uncertainties in High-Stakes Applications

AI Paper Summary
Large language models (LLMs) face a significant challenge in accurately representing uncertainty over the correctness of their output. This issue is critical for decision-making...

SelfGoal: An Artificial Intelligence AI Framework to Enhance an LLM-based Agent’s Capabilities to Achieve High-Level Goals

AI Paper Summary
Large language models (LLMs) have enabled the creation of autonomous language agents capable of solving complex tasks in dynamic environments without task-specific training. However,...

GenAI-Arena: An Open Platform for Community-Based Evaluation of Generative AI Models

AI Paper Summary
Generative AI has made remarkable progress in revolutionizing fields like image and video generation, driven by innovative algorithms, architectures, and data. However, the rapid...

Benchmarking Federated Learning for Large Language Models with FedLLM-Bench

AI Paper Summary
Large language models (LLMs) have achieved remarkable success across various domains, but training them centrally requires massive data collection and annotation efforts, making it...

Advancing Reliable Question Answering with the CRAG Benchmark

AI Shorts
Large Language Models (LLMs) have revolutionized Natural Language Processing (NLP), particularly in Question Answering (QA). However, hallucination remains a significant obstacle as LLMs may...

From Low-Level to High-Level Tasks: Scaling Fine-Tuning with the ANDROIDCONTROL Dataset

AI Paper Summary
Large language models (LLMs) have shown promise in powering autonomous agents that control computer interfaces to accomplish human tasks. However, without fine-tuning on human-collected...

The Missing Piece: Combining Foundation Models and Open-Endedness for Artificial Superhuman Intelligence ASI

AI Paper Summary
Recent advances in artificial intelligence, primarily driven by foundation models, have enabled impressive progress. However, achieving artificial general intelligence, which involves reaching human-level performance...

Researchers at UC Berkeley Propose a Neural Diffusion Model that Operates on Syntax Trees for Program Synthesis

AI Paper Summary
Large language models (LLMs) have revolutionized code generation, but their autoregressive nature poses a significant challenge. These models generate code token by token, without...

Modeling Cultural Accumulation in Artificial Reinforcement Learning Agents

AI Paper Summary
Cultural accumulation, the ability to learn skills and accumulate knowledge across generations, is considered a key driver of human success. However, current methodologies in...
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