Pranay Reddy

🌟 Exciting News! 🌟 Thrilled to share that our latest paper, "On Evaluating Black-Box Explainable AI Methods for Enhancing Anomaly Detection in Autonomous Driving Systems," has been published in MDPI Sensors Journal! In this work, we introduce a comprehensive framework to assess black-box XAI techniques like SHAP and LIME for anomaly detection in autonomous vehicles. Our study evaluates these methods across six key metrics, providing valuable insights into their effectiveness and limitations. A big thank you to my amazing co-authors OSVALDO ARRECHE and Mustafa Abdallah for their hard work and collaboration. Read the full paper here: https://lnkd.in/dqkUdEQ2 #AutonomousDriving #ExplainableAI #AnomalyDetection #XAI #AI #Cybersecurity #Research

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Day 5 of learning in public 🚀 Traditional fine-tuning methods for large language models often face challenges like high computational requirements, catastrophic forgetting, and single-task specialization. Enter LoRA (Low-Rank Adaptation), a game-changing technique that addresses these limitations, enabling efficient and practical fine-tuning of large language models. LoRA introduces a novel approach to fine-tuning by adding a small number of task-specific weights to the pre-trained model instead of updating all the weights. Here's how it works: 1. Low-Rank Matrices: For each layer in the pre-trained model, LoRA introduces a pair of low-rank matrices: one for the weights and one for the biases. 2. Adaptation: During fine-tuning, LoRA updates these low-rank matrices instead of modifying the pre-trained weights directly. 3. Composition: At inference time, the low-rank matrices are composed with the pre-trained weights to produce the final, adapted weights for the fine-tuned model. The low-rank nature of these matrices is the key to LoRA's efficiency, dramatically reducing the memory and computational requirements for fine-tuning. This makes it possible to adapt large language models even on consumer-grade hardware like CPUs or edge devices. But LoRA's advantages don't stop there. It can match or even outperform the performance of full fine-tuning while using far fewer updatable parameters. Additionally, the LoRA updates are reversible, allowing the model to revert to its original state, and composable, enabling a single pre-trained model to be fine-tuned for multiple tasks simultaneously. To illustrate the power of LoRA, let's dive into a practical example. Here, I'll demonstrate how to implement LoRA from scratch and fine-tune the DistilBERT model using LoRA on the IMDB dataset for sentiment analysis. https://lnkd.in/g-Tz2NU7 As you can see, LoRA is a remarkable technique that addresses the limitations of traditional fine-tuning methods, making it more efficient, practical, and versatile. By introducing low-rank, task-specific updates, LoRA enables efficient adaptation while preserving the model's original knowledge and allowing for multi-task specialization. As the demand for deploying large language models on resource-constrained devices continues to grow, techniques like LoRA will make these powerful models more accessible and practical.

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IndicGenBench Google Research India recently released IndicGenBench, a multilingual benchmark to evaluate generation capabilities of LLMs on 29 Indic languages spanning 13 writing scripts and 4 language families. Extended the datasets in Cross-lingual Summarization, Machine Translation, Multi-lingual Question Answering, and Cross-lingual Question Answering, the team has collected human translations for English examples into target Indic languages, thereby extending the scope and applicability of evaluation metrics in this domain. One of the key insights from their study is the analysis of token fertility across all Indic languages within IndicGenBench. Token fertility, representing the average number of sub-words that a word is broken down into by the tokenizer, varies significantly across languages Some languages have simple breakdowns, while others are more complex. Now, why does this matter? Well, it affects how well the language models work. Languages with more complex breakdowns might struggle because they can't use as many examples to learn from. They found that languages with simpler breakdowns can use more examples effectively compared to those with complex breakdowns. #LLMs #GenAI #IndicGenBench #AI #IndicLanguages #IndicDatasets #multilingual

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🚨ACL 2024 Outstanding Paper Award Alert 🚨 ➡️Paper Title: INDICLLMSUITE: A BLUEPRINT FOR CREATING PRE-TRAINING AND FINE-TUNING DATASETS FOR INDIAN LANGUAGES 🌟Few pointers from the paper 🎯Despite the considerable advancements in English LLMs, the progress in building comparable models for other languages has been hindered due to the scarcity of tailored resources. 👁In this paper Authors work aims to bridge this divide by introducing an expansive suite of resources specifically designed for the development of Indic LLMs, covering 22 languages, containing a total of 251B tokens and 74.8M instruction-response pairs. 🎯Recognizing the importance of both data quality and quantity, their approach combines highly curated manually verified data, unverified yet valuable data, and synthetic data. 🎯They built a clean, open-source pipeline for curating pre-training data from diverse sources, including websites, PDFs, and videos, incorporating best practices for crawling, cleaning, flagging, and deduplication. 🎯For instruction-fine tuning, they amalgamate existing Indic datasets, translate/transliterate English datasets into Indian languages, and utilize LLaMa2 and Mixtral models to create conversations grounded in articles from Indian Wikipedia and Wikihow. 🎯Additionally, they addressed toxicity alignment by generating toxic prompts for multiple scenarios and then generated non-toxic responses by feeding these toxic prompts to an aligned LLaMa2 model. 🎯 They hope that the datasets, tools, and resources released as a part of this work will not only propel the research and development of Indic LLMs but also establish an open-source blueprint for extending such efforts to other languages. 🏢Organization: 🛎️Nilekani Centre at AI4Bhārat🛎️ Indian Institute of Technology, Madras 🛎️Microsoft🛎️Sant Longowal Institute of Engineering and Technology 🛎️Indian Institute of Information Technology Design & Manufacturing Kancheepuram, 🛎️Maharaja Agrasen Institute Of Technology, Delhi 🛎️Sarvam ,🛎️ NICT 🧙Paper Authors: Mohammed Safi Ur Rahman Khan, Priyam Mehta , Ananth Sankar , Umashankar Kumaravelan, Sumanth Doddapaneni, Suriyaprasaad G, Varun Balan, Sparsh Jain, Anoop Kunchukuttan, Pratyush Kumar, Raj Dabre, Mitesh Khapra 1️⃣Read the Full Paper here: https://lnkd.in/gqwQST-r 2️⃣Code: https://lnkd.in/giWPVntx 🎉 🎉  Congratulations to all the authors 🎉 🎉 Find this Valuable 💎 ? ♻️REPOST and teach your network something new Follow me 👣, Naveen Manwani, for the latest updates on Tech and AI-related news, insightful research papers, and exciting announcements. #ACL2024 #Indicllms #llms #llama2 #mixtral

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🚨ECCV 2024 Paper Alert 🚨 ➡️Paper Title: T-MAE: Temporal Masked Autoencoders for Point Cloud Representation Learning 🌟Few pointers from the paper 🎯The scarcity of annotated data in LiDAR point cloud understanding hinders effective representation learning. Consequently, scholars have been actively investigating efficacious self-supervised pre-training paradigms. 🎯Nevertheless, temporal information, which is inherent in the LiDAR point cloud sequence, is consistently disregarded. 🎯To better utilize this property, authors of this paper proposed an effective pre-training strategy, namely “Temporal Masked Auto-Encoders (T-MAE)”, which takes as input temporally adjacent frames and learns temporal dependency. 🎯A SiamWCA backbone, containing a Siamese encoder and a windowed cross-attention (WCA) module, is established for the two-frame input. 🎯Considering that the movement of an ego-vehicle alters the view of the same instance, temporal modeling also serves as a robust and natural data augmentation, enhancing the comprehension of target objects. SiamWCA is a powerful architecture but heavily relies on annotated data. 🎯Their T-MAE pre-training strategy alleviates its demand for annotated data. Comprehensive experiments demonstrate that T-MAE achieves the best performance on both Waymo and ONCE datasets among competitive self-supervised approaches. 🏢Organization:  University of Amsterdam 🧙Paper Authors: Weijie Wei, Fatemeh Karimi Nejadasl, PhD, Theo Gevers, Martin R. Oswald 1️⃣Read the Full Paper here: https://lnkd.in/gKMuGJM5 2️⃣Project Page: https://lnkd.in/g5CC6a7H 3️⃣Code: https://lnkd.in/gHXXt6Gf 🎥 Be sure to watch the attached Presentation - Sound on 🔊🔊 Find this Valuable 💎 ? ♻️REPOST and teach your network something new Follow me 👣, Naveen Manwani, for the latest updates on Tech and AI-related news, insightful research papers, and exciting announcements. #ECCV2024 #lidarpointcloud #selfsupervisedlearning

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Training LLMs using reinforcement learning using human feedback. I was slacking on this topic for 6 months and finally was able to go through it, and wow I am amazed at what technologies are being worked on in production-ready systems these days. Generally in RL of LLMs, we have a reward model (let's say a good/bad text classifier) that gives a reward (prediction 1 for good, -1 for bad) and an RL algorithm (based on multiple loss functions) updates the weights of the LLM model to achieve better results. This is an iterative process between the reward model, the LLM, and the RL algorithm. Overall, my take is the most important aspect of training LLM using RLHM is obtaining that human feedback and training a reward model.

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It’s been an honor working with Gil Weinberg! Thanks so much! This demo is from one of the chapters in my thesis on #AI driven raga agnostic automatic accompaniment for alapana (non-rhythmic improvisations) in South Indian Classical Music. Since the algorithm is raga agnostic, the system can accompany for any raga performed! More demos on a violin playing #robot coming soon! #MusicTechnology #MachineLearning #HumanRobotInteraction #HumanComputerInteraction #AutomaticAccompaniment

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Comet - LLM model monitoring, trace every step along with the evaluation & ranking to see which models provide more value for our problem is really an advantage to stay ahead in the solution offering process.

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🎉 Excited to Share a New Achievement! 🎉 I’m thrilled to have earned the NVIDIA Generative AI LLMs (NCA-GENL) Certification! 🚀 This certification validates my foundational understanding of developing, integrating, and maintaining AI-driven applications using Generative AI and Large Language Models (LLMs) with NVIDIA solutions. Over the past few months, I’ve delved deep into a variety of resources, including courses and webinars on topics such as Hugging Face's NLP, Building RAG Agents for LLMs, Diffusion Models, DeepLearning.AI's Prompt Engineering, and LangChain for LLM Application Development, to name a few. The journey has been challenging, but incredibly rewarding. I’m particularly excited about the knowledge I’ve gained regarding NVIDIA’s AI tech stack, including DGX, AI Enterprise, NeMo, NIM, and TensorRT. This certification not only strengthens my understanding of the current AI landscape but also equips me with the skills to contribute to developing cutting-edge AI applications. A big thank you to all the amazing resources and communities that helped me along the way. I look forward to applying this knowledge to future projects and continuing to grow in this exciting field! #NVIDIA #GenerativeAI #LLMs #Certification #AI #DeepLearning #MachineLearning #LangChain #HuggingFace

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EMNLP 2024 results are out, we got in three papers, congrats to students :) First paper of many, Vatsal, Pranshu, Suyash, Janit, Kunal, Srija and Adnan, Congrats 🎉 🎊 🍾 🎈 Evaluating Concurrent Robustness of Language Models Across Diverse Challenge Sets (main) - Vatsal Gupta, PRANSHU PANDYA, Tushar Kataria, Vivek Gupta, Dan Roth tldr: make models robust on multiple adversarial challenge set simultaneously  arxiv: https://lnkd.in/ei-65Y6g Knowledge-Aware Reasoning over Multimodal Semi-structured Tables (finding) Suyash Vardhan Mathur, Jainit Bafna, Kunal Kartik, Harshita Khandelwal, Manish Shrivastava, Vivek Gupta, Mohit Bansal, Dan Roth tldr: introduce the novel task of reasoning on multimodal tabular data, text + images in a structure (first of a kind) arxiv: https://lnkd.in/e5ycusfn   Unraveling the Truth: Do LLMs really Understand Charts? A Deep Dive into Consistency and Robustness (finding) Srija Mukhopadhyay, Adnan Qidwai, Aparna Garimella, Pritika Ramu, Vivek Gupta, Dan Roth           tldr: VLM although seem very accurate on chart, are not all robust and consistent in performance arxiv: https://lnkd.in/eFFA-sVX We also got an workshop paper, at BlockBoxNLP 2024 @EMNLP, here the details Enhancing Question Answering on Charts Through Effective Pre-training Tasks Ashim Gupta, Vivek Gupta, Shuo Zhang, Yujie He, Ning Zhang , Shalin Shah, Ph.D. tldr: models is not robust & consistent on charts, can we use pre-training on simple auxiliary tasks to enhance model robustness arxiv: https://lnkd.in/egbRHm-4

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🚀 Excited to present our work on "AdaptAgent: Adapting Multimodal Web Agents with Few-Shot Learning from Human Demonstrations" at the #NeurIPS2024 Workshop on Adaptive Foundation Models! 📅 December 14, 2024, 10:45 AM 📍 West Exhibition Hall A 🔴 Problem: Multimodal web agents struggle to adapt to unseen tasks/websites/domains, relying on large-scale pre-training and fine-tuning, which limits efficiency. 🛠️ Methods: We introduce AdaptAgent, enabling agents to adapt with few-shot human demonstrations via in-context learning for proprietary models and meta-learning for open-weight models. 📈 Results & Importance: AdaptAgent boosts task success by a relative increase of 21.03% to 65.75% with just 1-2 multimodal demonstrations, offering a more efficient, adaptable solution for web agents. 📄 For more details, check out our arxiv paper: https://lnkd.in/g46s2Pys. This work is led by our intern Gaurav Verma during his time at J.P. Morgan AI Research, along with fellow collaborators Nishan S., Zhen Zeng, Tucker Richard Balch, and Manuela Veloso ❤️🤝 PS: We’re also looking for an intern in multimodal agents! 🤖 If you're passionate about multimodal systems, reach out (here or at NeurIPS 2024) or apply: https://lnkd.in/gtN6P5w9. #MultimodalAgents #FewShotLearning #JobOpening

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We have released a brand-new lecture series on Physics Informed Neural Networks (PINN) on Vizuara's YouTube channel: https://lnkd.in/gegNBDV6 Last month we received a message from Vivek Karmarkar and we were discussing collaboration opportunities in the field of Scientific ML. Vivek mentioned his vision: Why don't we make a YouTube series on "Physics Informed Neural Networks (PINNs) using JAX" Physics Informed Neural Networks is that branch of Scientific Machine Learning which has led to a lot of innovative research by mixing physics with neural networks. It has transformed many fields of science and engineering from battery modeling to epidemiology to fluid mechanics! JAX is a computing library developed by Google. We were very happy with this idea proposed by Vivek, because PINNs and JAX could totally transform a student's career. We started this collaboration. Vivek worked very hard over the past month to make a detailed plan for his series of lectures. He teaches everything from a fundamental level, and I was very happy to see his explanations grounded in the beauty of physics and mathematics. Yesterday, we were very happy to release the first lecture made by Vivek: "Introduction to Physics Informed Neural Networks using JAX | A hands on, project based course": You can access this lecture here: https://lnkd.in/gQBamAbU This lecture has received an incredible response so far. Learning and teaching ML topics every day gives a purpose to the day. It takes us closer to our vision of making AI accessible to all. Great job Vivek!

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NLP (LLM) Research Roadmap I’m happy to be an NLP research mentor for - PhD candidates without a good faculty supervisor - Industry professionals who want to gain research experience by publishing papers. Guidance will be provided via 1-to-1 paid Google Meet sessions. Interested people can DM me with all the relevant details. "Hi" or "incomplete messages" will be ignored due to time constraints. NLP research involves various phases like - Research Foundation (learning pre-requisites) - Research Papers Reading - Research Problem Identification - Research Gaps Identification - Research Idea Formulation - Research Idea Implementation - Research Paper Writing - Research Paper Submission and Review A good faculty supervisor provides proper guidance in every phase of NLP research. Unfortunately, some faculty supervisors will never do this which makes the journey of PhD candidates difficult. Due to a lack of proper guidance from the faculty supervisor, some of the PhD candidates despite being highly talented finish their PhD with average or below-average papers even after spending more than five years. #llms #nlproc #india #phdresearch #phd

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OpenAI has announced the release of o1 model which contains chain-of-thoughts (CoT) Critics says that openAI were already using it before. LLMs, reinforcement learning (RL) and CoT is coming together in o1. o1 models introducing inference compute over pretraining & fine tuning paradigm. Instead of solely depending on the trained data, o1 can "think" before generating output using RL and CoT. CoT prompting simulates human-like reasoning by breaking down complex tasks into a sequence of logical steps, allowing models to articulate their thought processes. #openai #openaio1 #reinforcementlearning #chainofthoughts

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🔎 Demystifying LLMs: A New Video Series! 🔎 Struggling to wrap your head around Large Language Models (LLMs)? I feel you! That's why I'm launching a new video series, LLM Explained, in collaboration with Uthiralakshmi S. from Worcester Polytechnic Institute. Each episode will be a bite-sized 2-minute explainer, tackling a specific LLM concept. This week, we're diving into the foundations: tokenization and embedding! Whether you're a seasoned data scientist or just starting your AI journey, this series is designed to make LLMs accessible and engaging.✨ Stay tuned for the next episode! In the meantime, hit that follow button and join the conversation! If you learned something valuable, share it with your network! 👨‍💻 👥 #GenAI #LLM #LargeLanguaeModels #DeepLearning #NeuralNetwork #AI #MachineLearning #Tokens #Tokenization #Embedding #Education #AIEducation #Data #DataScience #NEU #WPI

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We had a great session about NVIDIA NIMs with Anshul Jindal and Mark Moyou, PhD yesterday. 🚀 From Mark: We got lots of info on inferencing with NIMs and different types of NIMs like embedding models, generative models, reranking models.. 🎨 I walked through how you can use NIMs for embedding, retrieval and generation with #Haystack 🚀 And finally, Anshul did a live coding session where he showed how you can deploy and scale your own self-hosted NIMs. If you missed it, or don't even know what I'm talking about but you wanna learn, the recording is up (adding to the comments👇) All resources, including the example repos and guides are in the description. #AI #LLM #Nvidia #opensource

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I remember when this all started. Faris - invited me to catch up at our favorite spot around Blok M. I had no idea he’d just wrapped up a procedure at Pondok Indah Hospital! (He really knows how to add drama to our catch-ups.) We’d been on separate paths and projects since "the winding down project", but there he was, reminding me of our dream to ride this AI wave together again. It was a plan we’d hatched with Sahal Zain & Gurnoor Dhillon when the world of AI was still mostly theoretical and jargon-heavy. Somehow, the conversation continued with Gurnoor Dhillon while he was waiting for his meeting with someone who would eventually become one of CalvinBall’s key early adopters. It felt like everything was coming together, like a series of seemingly random dots connecting to form a bigger picture. Seeing how far we’ve come as a team at CalvinBall Technologies is truly exciting. What started as theoretical discussions has now evolved into practical Gen AI solutions that are solving everyday problems and hold endless possibilities to shape the industry. 👨‍🚀 👩‍🚀 Here’s to pushing boundaries and achieving new milestones together! Gurnoor Dhillon, Sandeep Ramesh, Faris -, Sahal Zain, Eva Zuliya, Ratu Nadiah Khairunnisa 🚀 #GenAI #AIInnovation #TechJourney #ArtificialIntelligence #TechTransformation #CalvinballTech #Calvinballers

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