Badri N. Patro Profile
Badri N. Patro

@badripatro_iitb

Followers
265
Following
4K
Media
13
Statuses
2K

Senior Research Scientist @Microsoft | Prev: @Google, @Microsoft, @Samsung, @HARMAN | | PDF @KU_Leuven | PhD @IITKanpur | https://t.co/cNrlVQCXxw. @iitbombay

India
Joined October 2015
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@PBSHABD
PB-SHABD
5 months
#WATCH : प्रधानमंत्री नरेंद्र मोदी ने मन की बात कार्यक्रम में मेघालय के एरी सिल्क को GI टैग मिलने पर बधाई दी। उन्होंने इसे 'अहिंसा सिल्क' बताते हुए इसकी पर्यावरण–अनुकूलता और वैश्विक मांग की चर्चा की। ख़बर विस्तार से पढ़ने के लिए जुड़ें #PBSHABD के साथ, अभी जुड़ने के लिए क्लिक
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@CadioArena
Cardio Arena
8 months
Home Maintenance Hacks: DIY Skills Repair Technicians Don't Want You to Know!👨‍🔧 Tag your hubby and let him in on these handy tips🧤👇👇
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@DailyDoseOfDS_
Daily Dose of Data Science
9 months
Transformers vs Mixture of experts in LLMs visually explained:
@_avichawla
Avi Chawla
9 months
Transformer vs. Mixture of Experts in LLMs, clearly explained (with visuals):
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@ICCVConference
#ICCV2025
9 months
#ICCV2025 reminders - All qualified authors are required to act as reviewers. - Any reviewer who fails to submit their assigned reviews by the deadline will face a desk rejection of all papers on which they are an author per the discretion of the PCs. https://t.co/rBRTjduBcx
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@Bhaskar_m11
Bhaskar Mishra
10 months
दक्षिण भारत में बनी एक फिल्म का सीन है ! आपकी आँखें फटी की फटी रह जाएँगीं, बॉलीवुड तो ऐसी फिल्म क्या.. इस सीन जैसा भी कभी कुछ नहीं दिखा पाएगा ! क्योंकि वो बॉलीवुड नहीं रहा👍 पूरी दुनिया में दिखाने लायक सीन फिल्माया गया है। जो 100%सही है। सभी देखे और भेजे आगे से आगे।आपका आभार
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@ggerganov
Georgi Gerganov
10 months
CPU is all you need
@carrigmat
Matthew Carrigan
10 months
Complete hardware + software setup for running Deepseek-R1 locally. The actual model, no distillations, and Q8 quantization for full quality. Total cost, $6,000. All download and part links below:
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@thesigmamindset
The Sigma Mindset
10 months
If you want to speak like a leader, watch this ‼️‼️
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@Avinasjain
Avinash Jain
10 months
इस शख्स को इतनी भयंकर जानकारी आप सुनकर दंग रह जाएंगे। एक बार जरूर सुने !
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@a_vijaysrinivas
Dr. Vijay Srinivas A
11 months
Glad to announce our paper named “SiMBA-TS: Looking Beyond Transformers for Long Term Time-Series Forecasting” which has been accepted in the ICASSP 2025 ( https://t.co/SWHvbABscw) A short summary of the paper: Thanks @badripatro_iitb, my co-author https://t.co/wgRecgyuEB
lnkd.in
This link will take you to a page that’s not on LinkedIn
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@LangChainAI
LangChain
10 months
📄🤖 PDF Q&A with Deepseek Create a production-ready AI chatbot that answers questions from PDFs using DeepSeek's LLM and LangChain's document processing. Features intelligent text processing, advanced reasoning, and an intuitive interface. https://t.co/pJY6YB7ZX1
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@cneuralnetwork
neural nets.
10 months
Perplexity has announce free Perplexity Pro for the below colleges, if you belong to them, do get your free Pro - Indian Institute of Technology, Kharagpur - Indian Institute of Technology, Kanpur - Indian Institute of Technology, Delhi - Indian Institute of Technology,
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@_akhaliq
AK
10 months
10x speed looks wild Deepseek R1 coder + OS OpenAI Operator Agent
@_akhaliq
AK
10 months
DeepSeek-R1 coder + OS OpenAI Operator Agent (browser-use) prompt: Go to https://huggingfacedotco/spaces/akhaliq/anychat click deepseek coder in dropdown, type write a script for a bouncing yellow ball within a sphere, make sure to handle collision detection properly. then
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@deedydas
Deedy
10 months
DeepSeek built a high performance computer on Aug 31 with 10,000 A100 GPUs. A must-read paper only cited ONCE. In their V3 paper, the base for R1, they say they train on 2048 H800s, the export-controlled H100 with 50% the transfer rate. Why didn't they use the 10,000 A100s?
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@dair_ai
DAIR.AI
10 months
10). Agentic RAG Overview Provides a comprehensive introduction to LLM agents and Agentic RAG. It provides an exploration of Agentic RAG architectures, applications, and implementation strategies. https://t.co/x1dCnZgbyc
@omarsar0
elvis
10 months
Agentic RAG Overview This is a great intro to LLM agents and Agentic RAG. It provides a comprehensive exploration of Agentic RAG architectures, applications, and implementation strategies.
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@dair_ai
DAIR.AI
10 months
Here are the top AI Papers of the Week (Jan 20-26): - DeepSeek-R1 - Can LLMs Plan? - Chain-of-Agents - Scaling RL with LLMs - Humanity’s Last Exam - Agentic RAG Overview Read on for more:
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@hardmaru
hardmaru
11 months
Transformer²: Self-adaptive LLMs https://t.co/VnvzSmeIad This new paper from @SakanaAILabs shows the power of an LLM that can self-adapt its weights to its environment. I think in the future, the line between “pre-training” and “post-training” will be gone, and our models and
@SakanaAILabs
Sakana AI
11 months
We’re excited to introduce Transformer², a machine learning system that dynamically adjusts its weights for various tasks! https://t.co/ci028qPUWt Adaptation is a remarkable natural phenomenon, like how the octopus can blend in with its environment, or how the brain rewires
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@KirkDBorne
Kirk Borne
1 year
[Download 698-page PDF eBook] Everything You Always Wanted To Know About #Mathematics* (*But didn’t even know to ask) A Guided Journey Into the World of Abstract Mathematics, Theorems, and the Writing of Proofs: https://t.co/JLsDOmpP1q
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@KirkDBorne
Kirk Borne
1 year
[Download 496-page PDF eBook] Applied Causal #Inference Powered by #MachineLearning and #AI: https://t.co/NPkNAHaB47 ———— #ML #DataScience #Algorithms #Statistics
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@Sumanth_077
Sumanth
1 year
Pandas is a powerful data analysis and manipulation library for Python! NVIDIA just made Pandas 150x faster with zero code changes 🔥 All you have to add is just a couple of lines of code: %load_ext cudf.pandas import pandas as pd
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@Yampeleg
Yam Peleg
1 year
Damnnnn this tutorial is of high quality!! Check out the code repository: - Standalone Jupyter notebooks. - Only what you need. - Highly explanatory. - 0 over engineering. - All from scratch. - Self contained. I wish everyone wrote all their code like this.
@rasbt
Sebastian Raschka
1 year
Direct Preference Optimization (DPO) has become one of the go-to methods to align large language models (LLMs) more closely with user preferences. If you want to learn how it works, I coded it from scratch: https://t.co/VioT1zVn68
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