:::info Authors: (1) Martyna Wiącek, Institute of Computer Science, Polish Academy of Sciences; (2) Piotr Rybak, Institute of Computer Science, Polish Academy of Sciences; (3) Łukasz Pszenny, Institute of Computer Science, Polish Academy of Sciences; (4) Alina Wróblewska, Institute of Computer Science, Polish Academy of Sciences. ::: :::tip Editor’s note: This is Part 8 of … Read more
:::info Authors: (1) Martyna Wiącek, Institute of Computer Science, Polish Academy of Sciences; (2) Piotr Rybak, Institute of Computer Science, Polish Academy of Sciences; (3) Łukasz Pszenny, Institute of Computer Science, Polish Academy of Sciences; (4) Alina Wróblewska, Institute of Computer Science, Polish Academy of Sciences. ::: :::tip Editor’s note: This is Part 7 of … Read more
:::info Authors: (1) Martyna Wiącek, Institute of Computer Science, Polish Academy of Sciences; (2) Piotr Rybak, Institute of Computer Science, Polish Academy of Sciences; (3) Łukasz Pszenny, Institute of Computer Science, Polish Academy of Sciences; (4) Alina Wróblewska, Institute of Computer Science, Polish Academy of Sciences. ::: :::tip Editor’s note: This is Part 6 of … Read more
:::info Authors: (1) Martyna Wiącek, Institute of Computer Science, Polish Academy of Sciences; (2) Piotr Rybak, Institute of Computer Science, Polish Academy of Sciences; (3) Łukasz Pszenny, Institute of Computer Science, Polish Academy of Sciences; (4) Alina Wróblewska, Institute of Computer Science, Polish Academy of Sciences. ::: :::tip Editor’s note: This is Part 5 of … Read more
:::info Authors: (1) Martyna Wiącek, Institute of Computer Science, Polish Academy of Sciences; (2) Piotr Rybak, Institute of Computer Science, Polish Academy of Sciences; (3) Łukasz Pszenny, Institute of Computer Science, Polish Academy of Sciences; (4) Alina Wróblewska, Institute of Computer Science, Polish Academy of Sciences. ::: :::tip Editor’s note: This is Part 4 of … Read more
:::info Authors: (1) Martyna Wiącek, Institute of Computer Science, Polish Academy of Sciences; (2) Piotr Rybak, Institute of Computer Science, Polish Academy of Sciences; (3) Łukasz Pszenny, Institute of Computer Science, Polish Academy of Sciences; (4) Alina Wróblewska, Institute of Computer Science, Polish Academy of Sciences. ::: :::tip Editor’s note: This is Part 3 of … Read more
:::info Authors: (1) Martyna Wiącek, Institute of Computer Science, Polish Academy of Sciences; (2) Piotr Rybak, Institute of Computer Science, Polish Academy of Sciences; (3) Łukasz Pszenny, Institute of Computer Science, Polish Academy of Sciences; (4) Alina Wróblewska, Institute of Computer Science, Polish Academy of Sciences. ::: :::tip Editor’s note: This is Part 2 of … Read more
:::info Authors: (1) Martyna Wiącek, Institute of Computer Science, Polish Academy of Sciences; (2) Piotr Rybak, Institute of Computer Science, Polish Academy of Sciences; (3) Łukasz Pszenny, Institute of Computer Science, Polish Academy of Sciences; (4) Alina Wróblewska, Institute of Computer Science, Polish Academy of Sciences. ::: :::tip Editor’s note: This is Part 1 of … Read more
How are you, hacker? 🪐Want to know what’s trending right now?: The Techbeat by HackerNoon has got you covered with fresh content from our trending stories of the day! Set email preference here. ## Future-proof Your Marketing With This Guide on Writing for AI Search Engines By @darragh [ 4 Min read ] Learn how … Read more
Table of Links Abstract and Introduction Related work Methods 3.1 Positive-sum fairness 3.2 Application Experiments 4.1 Initial results 4.2 Positive-sum fairness Conclusion and References 2 Related work Bias is commonly identified in medical image analysis applications [38,40]. For instance [6], a CNN trained on brain MRI resulted in a significant difference between ethnicities. Seyyed-Kalantari et … Read more
:::info Authors: (1) Samia Belhadj∗, Lunit Inc., Seoul, Republic of Korea (samia.belhadj@lunit.io); (2) Sanguk Park [0009 −0005 −0538 −5522]*, Lunit Inc., Seoul, Republic of Korea (tony.superb@lunit.io); (3) Ambika Seth, Lunit Inc., Seoul, Republic of Korea (ambika.seth@lunit.io); (4) Hesham Dar [0009 −0003 −6458 −2097], Lunit Inc., Seoul, Republic of Korea (heshamdar@lunit.io); (5) Thijs Kooi [0009 −0003 … Read more
Table of Links Abstract and 1 Introduction 2. Related Work 3. LLaVA-Phi and 3.1. Training 3.2. Qualitative Results 4. Experiments 5. Conclusion, Limitation, and Future Works and References 5. Conclusion, Limitation, and Future Works We introduce LLaVA-Phi, a vision language assistant developed using the compact language model Phi-2. Our work demonstrates that such small vision-language … Read more
In a world dominated by connectivity, we rely on GPS for everything from navigating city streets to tracking the arrival of our food delivery. But most of us don’t give much thought to how it all works—until it doesn’t. Whether it’s your Uber driver getting lost in a crowded urban area, your delivery package arriving … Read more
:::info Authors: (1) Yichen Zhu, Midea Group; (2) Minjie Zhu, Midea Group and East China Normal University; (3) Ning Liu, Midea Group; (4) Zhicai Ou, Midea Group; (5) Xiaofeng Mou, Midea Group. ::: Table of Links Abstract and 1 Introduction 2. Related Work 3. LLaVA-Phi and 3.1. Training 3.2. Qualitative Results 4. Experiments 5. Conclusion, … Read more
Dr. Saifedean Ammous has authored the most read Bitcoin book in the world – The Bitcoin Standard. Now, he is preparing to unleash his dragons onto the fictional scene with The Gold Standard. For more information about this next book, check out his tweet. He provides the first 4 chapters free at his website and … Read more
Table of Links Abstract and 1 Introduction 2. Related Work 3. LLaVA-Phi and 3.1. Training 3.2. Qualitative Results 4. Experiments 5. Conclusion, Limitation, and Future Works and References 4. Experiments We rigorously evaluated LLaVA-Phi using an extensive array of academic benchmarks specifically designed for multi-modal models. These included tests for general question-answering such as VQA-v2 … Read more
Table of Links Abstract and 1 Introduction 2 Background and 2.1 Transformer-Based Large Language Models 2.2 LLM Service & Autoregressive Generation 2.3 Batching Techniques for LLMs 3 Memory Challenges in LLM Serving 3.1 Memory Management in Existing Systems 4 Method and 4.1 PagedAttention 4.2 KV Cache Manager 4.3 Decoding with PagedAttention and vLLM 4.4 Application … Read more
How are you, hacker? 🪐 What’s happening in tech today, December 29, 2024? The HackerNoon Newsletter brings the HackerNoon homepage straight to your inbox. On this day, The HMS Warrior is Launched in 1860, The Apex of the Japanese Asset Price Bubble Occured in 1989, The UK settled its Anglo-American, post WWII loan debt in … Read more
Table of Links Abstract and 1 Introduction 2 Background and 2.1 Transformer-Based Large Language Models 2.2 LLM Service & Autoregressive Generation 2.3 Batching Techniques for LLMs 3 Memory Challenges in LLM Serving 3.1 Memory Management in Existing Systems 4 Method and 4.1 PagedAttention 4.2 KV Cache Manager 4.3 Decoding with PagedAttention and vLLM 4.4 Application … Read more
Money is not the only tool for employee’ motivation. Supplementing financial incentives with something intangible helps make employees more loyal to the company and maintains their productivity over the long term. Management is currently performing a balancing act: They must deliver the right amount of mentorship, steer corporate culture and offer attractive compensation while introducing … Read more
In the ==first part of this tutorial== series, we took the first steps for building an end-to-end MLOps pipeline using Databricks and Spark, guided by Databricks’ reference architecture. Here’s a recap of the key steps we covered: Setting Up the Unity Catalog for Medallion Architecture: We organized our data into bronze, silver, and gold layers … Read more
How are you, hacker? 🪐Want to know what’s trending right now?: The Techbeat by HackerNoon has got you covered with fresh content from our trending stories of the day! Set email preference here. ## RootstockCollective In-Depth: Empowering Bitcoin Builders By @rootstock_io [ 7 Min read ] Empowering Bitcoin builders with RootstockCollective DAO: Rewarding innovation, stakers, … Read more
Table of Links Abstract and Introduction Preliminaries Overview Protocol 4.1 Efficient Option Transfer Protocol 4.2 Holder Collateral-Free Cross-Chain Options Security Analysis 5.1 Option Transfer Properties 5.2 Option Properties Implementation Related Work Conclusion and Discussion, and References A. Codes A.1 Robust and Efficient Transfer Protocol A.2 Holder Collateral-Free Cross-Chain Options B. Proofs B.1 Transfer Protocol Proofs … Read more
Table of Links Abstract and Introduction Preliminaries Overview Protocol 4.1 Efficient Option Transfer Protocol 4.2 Holder Collateral-Free Cross-Chain Options Security Analysis 5.1 Option Transfer Properties 5.2 Option Properties Implementation Related Work Conclusion and Discussion, and References A. Codes A.1 Robust and Efficient Transfer Protocol A.2 Holder Collateral-Free Cross-Chain Options B. Proofs B.1 Transfer Protocol Proofs … Read more
Table of Links Abstract and 1 Introduction 2 Background and 2.1 Transformer-Based Large Language Models 2.2 LLM Service & Autoregressive Generation 2.3 Batching Techniques for LLMs 3 Memory Challenges in LLM Serving 3.1 Memory Management in Existing Systems 4 Method and 4.1 PagedAttention 4.2 KV Cache Manager 4.3 Decoding with PagedAttention and vLLM 4.4 Application … Read more
Table of Links Abstract and 1 Introduction 2. Related Work 3. LLaVA-Phi and 3.1. Training 3.2. Qualitative Results 4. Experiments 5. Conclusion, Limitation, and Future Works and References 3. LLaVA-Phi Our overall network architecture is similar to LLaVA-1.5. We use the pre-trained CLIP ViT-L/14 with a resolution of 336×336 as the visual encoder. A two-layer … Read more
Table of Links Abstract and 1 Introduction 2 Background and 2.1 Transformer-Based Large Language Models 2.2 LLM Service & Autoregressive Generation 2.3 Batching Techniques for LLMs 3 Memory Challenges in LLM Serving 3.1 Memory Management in Existing Systems 4 Method and 4.1 PagedAttention 4.2 KV Cache Manager 4.3 Decoding with PagedAttention and vLLM 4.4 Application … Read more
Table of Links Abstract and 1 Introduction 2 Background and 2.1 Transformer-Based Large Language Models 2.2 LLM Service & Autoregressive Generation 2.3 Batching Techniques for LLMs 3 Memory Challenges in LLM Serving 3.1 Memory Management in Existing Systems 4 Method and 4.1 PagedAttention 4.2 KV Cache Manager 4.3 Decoding with PagedAttention and vLLM 4.4 Application … Read more
Table of Links Abstract and 1 Introduction 2. Related Work 3. LLaVA-Phi and 3.1. Training 3.2. Qualitative Results 4. Experiments 5. Conclusion, Limitation, and Future Works and References 2. Related Work The rapid advancements in Large Language Models (LLMs) have significantly propelled the development of vision-language models based on LLMs. These models, representing a departure … Read more
Table of Links Abstract and 1 Introduction 2 Background and 2.1 Transformer-Based Large Language Models 2.2 LLM Service & Autoregressive Generation 2.3 Batching Techniques for LLMs 3 Memory Challenges in LLM Serving 3.1 Memory Management in Existing Systems 4 Method and 4.1 PagedAttention 4.2 KV Cache Manager 4.3 Decoding with PagedAttention and vLLM 4.4 Application … Read more