Author: Adeeba Alam Ansari

Adeeba Alam Ansari
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Adeeba Alam Ansari is currently pursuing her Dual Degree at the Indian Institute of Technology (IIT) Kharagpur, earning a B.Tech in Industrial Engineering and an M.Tech in Financial Engineering. With a keen interest in machine learning and artificial intelligence, she is an avid reader and an inquisitive individual. Adeeba firmly believes in the power of technology to empower society and promote welfare through innovative solutions driven by empathy and a deep understanding of real-world challenges.

JPMorgan Chase Researchers Propose JPEC: A Novel Graph Neural Network that Outperforms Expert’s Predictions on Tasks of Competitor Retrieval

AI Paper Summary
Knowledge graphs are finding their way into financial practices, especially as powerful tools for competitor retrieval tasks. Graph's ability to organize and analyze complex...

Researchers at Cambridge Provide Empirical Insights into Deep Learning through the Pedagogical Lens of Telescopic Model that Uses First-Order Approximations

AI Paper Summary
Neural networks remain a beguiling enigma to this day. On the one hand, they are responsible for automating daunting tasks across fields such as...

This AI Research Diagnoses Problems in Recurrent Neural Networks RNN-based Language Models and Corrects them to Outperform Transformer-based Models on Long Sequence Tasks

AI Paper Summary
Recurrent Neural Networks were the trailblazers in natural language processing and set the cornerstone for future advances. RNNs were simple in structure with their...

Researchers at KAUST Use Anderson Exploitation to Maximize GPU Efficiency with Greater Model Accuracy and Generalizability

AI Paper Summary
Escalation in AI implies an increased infrastructure expenditure. The massive and multidisciplinary research exerts economic pressure on institutions as high-performance computing (HPC)  costs an...

Researchers at Stanford Present ZIP-FIT : A Novel Data Selection AI Framework that Chooses Compression Over Embeddings to Finetune Models on Domain Specific Tasks

AI Paper Summary
Data Selection for domain-specific art is an intricate craft, especially if we want to get the desired results from Language Models. Until now, researchers...

LongPiBench: A Comprehensive Benchmark that Explores How Even the Top Large Language Models have Relative Positional Biases

AI Paper Summary
Accurate assessment of Large Language Models is best done with complex tasks involving long input sequences. Input sequence can exceed even 200,000 tokens in...

This AI Paper Explores If Human Visual Perception can Help Computer Vision Models Outperform in Generalized Tasks

AI Paper Summary
Human beings possess innate extraordinary perceptual judgments, and when computer vision models are aligned with them, model’s performance can be improved manifold. Various attributes...

Meta AI Releases Cotracker3: A Semi-Supervised Tracker that Produces Better Results with Unlabelled Data and Simple Architecture

AI Paper Summary
Point tracking is paramount in video; from 3d reconstruction to editing tasks, a precise approximation of points is necessary to achieve quality results. Over...

Quanda: A New Python Toolkit for Standardized Evaluation and Benchmarking of Training Data Attribution (TDA) in Explainable AI

AI Paper Summary
XAI, or Explainable AI, brings about a paradigm shift in neural networks that emphasizes the need to explain the decision-making processes of neural networks,...

IBM Researchers ACPBench: An AI Benchmark for Evaluating the Reasoning Tasks in the Field of Planning

AI Paper Summary
LLMs are gaining traction as the workforce across domains is exploring artificial intelligence and automation to plan their operations and make crucial decisions. Generative...

InstructG2I : A Graph Context Aware Stable Diffusion Model to Synthesize Images from Multimodal Attributed Graphs

AI Paper Summary
Multimodal Attributed Graphs (MMAGs) have received little attention despite their versatility in image generation. MMAGs represent relationships between entities with combinatorial complexity in a...

New Neural Warp Sampling Method Enhances Photorealistic Rendering: Reducing Variance and Improving Efficiency in Complex Material Interactions

AI Paper Summary
Monte Carlo Simulations take the spotlight when we discuss the photorealistic rendering of natural images. Photorealistic rendering, or, in layman’s words, creating indistinguishable “clones”...
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