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.

UBC Researchers Introduce ‘First Explore’: A Two-Policy Learning Approach to Rescue Meta-Reinforcement Learning RL from Failed Explorations

Reinforcement Learning is now applied in almost every pursuit of science and tech, either as a core methodology or to optimize existing processes and...

TIME Framework: A Novel Machine Learning Unifying Framework Breaking Down Temporal Model Merging

Model Merging allows one to leverage the expertise of specific fine-tuned models as a single powerful entity. The concept is straightforward: teach variants of...

DEIM: A New AI Framework that Enhances DETRs for Faster Convergence and Accurate Object Detection

Transformer-based Detection models are gaining popularity due to their one-to-one matching strategy. Unlike familiar many-to-One Detection models like YOLO, which require Non-Maximum Suppression (NMS)...

Auto-RAG: An Autonomous Iterative Retrieval Model Centered on the LLM’s Powerful Decision-Making Capabilities

Retrieval Augmented Generation is an efficient solution for knowledge-intensive tasks that improves the quality of outputs and makes it more deterministic with minimal hallucinations....

CPU-GPU I/O-Aware LLM Inference Reduces Latency in GPUs by Optimizing CPU-GPU Interactions

LLMs are driving major advances in research and development today. A significant shift has been observed in research objectives and methodologies toward an LLM-centric...

From Wordle to Robotics: Q-SFT Unleashes LLMs’ Potential in Sequential Decision-Making

Integration of Reinforcement Learning RL with large language models catalyzes LLM's performance on distinct specialty tasks such as robotics control or natural language processing...

Four Cutting-Edge Methods for Evaluating AI Agents and Enhancing LLM Performance

The advent of LLMs has propelled advancements in AI for decades. One such advanced application of LLMs is Agents, which replicate human reasoning remarkably....

Researchers from NVIDIA and MIT Present SANA: An Efficient High-Resolution Image Synthesis Pipeline that Could Generate 4K Images from a Laptop

Diffusion models have pulled ahead of others in text-to-image generation. With continuous research in this field over the past year, we can now generate...

Missingness-aware Causal Concept Explainer: An Elegant Explanation by Researchers to Solve Causal Effect Limitations in Black Box Interpretability

Concept-based explanations of machine learning applications have a greater intuitive appeal, as established by emerging research as an alternative to traditional approaches. Concept-driven methods...

Bidirectional Causal Language Model Optimization to Make GPT and Llama Robust Against the Reversal Curse

Despite their advanced reasoning capabilities, the latest LLMs often miss the mark when deciphering relationships. In this article, we explore the Reversal Curse, a...

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

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

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