PVML

Social media over the last five years has become a growing and dominant source of information and news for hundreds of millions of individuals all over the world. Social media is always in your pocket on your smartphone.

The Doe Discuss How Tech and Social Media Has Changed Journalism 

Explore how leveraging pre-trained vision-language models (PVLMs) revolutionizes hateful meme detection, achieving new milestones without fine-tuning.

Hateful Meme Detection: Leveraging PVLMs for Zero-shot VQA Probing

Learn about zero-shot visual question answering (VQA) and the use of pre-trained vision-language models (PVLMs) like BLIP-2 in bridging the modality gap.

Zero-Shot Visual Question Answering with PVLMs

Product management is an <strong>incredible</strong> career opportunity. There are few careers that allow employees to dabble in so many different fields. At Google, I worked with teams in marketing, engineering, design, data analysis, PR, user research, legal, business, quality assurance, and so many more.

Introducing PMLesson, Dynamic Product Management Interview Prep

A 12B parameter transformer trained on 250M image-text pairs generates high-quality, zero-shot images and emerging image-to-image capabilities.

DALL·E Research Paper: High Quality Image Generation Without Using Any of the Training Labels

Bundlers are the bridge between account abstraction and the execution layer.

A Developer's Guide to Building Next-Gen Smart Wallets With ERC-4337 — Part 2: Bundlers

Efficient, eco-friendly, and powerful — Sparse Spectral Training boosts LLM performance while cutting memory use and training costs. 

Can Sparse Spectral Training Make AI More Accessible? 

Datadog introduces Toto, an AI model for time series forecasting that boosts accuracy, reliability, and responsible observability.

Toto: Time Series Optimized Transformer for Observability

The main risk is a gradual transfer of product strategy from business leaders to technical systems—often without anyone deciding this should happen.

Why Product Teams Risk Surrendering Judgment to Algorithms

Explores GPT-3.5 and GPT-4 as simulators, highlighting their potential, limitations, and ethical risks in AI-driven simulations.

AI Models Can't Be Trusted in High-Stakes Simulations Just Yet

Multimodal hyper-embeddings enable strong cross-task transfer; tiny tuned modules match or beat full fine-tunes on unseen domains.

Cut 90% of Fine-Tuning Cost—Still Beat Baselines on Text and Vision Benchmarks

This article explains how to test for independence, homogeneity of variance, and normality in a pair programming study

Homogeneity and Normality: How to Test Your Experimental Data

Profile your TensorFlow models to find bottlenecks, optimize CPU/GPU usage, and speed up training with the TensorFlow Profiler & TensorBoard.

How to Use TensorFlow Profiler to Optimize Model Performance

R2-TFRM uses on-chain randomness to ensure fair, cost-efficient blockchain transactions—guaranteeing rebates and minimizing miner manipulation.

Reducing Blockchain Fees with R2-TFRM and On-Chain Randomness

Idefics2, an efficient 8B vision-language model, sets new multimodal AI benchmarks—while its release, dataset, and red-teaming highlight progress and risks.

How an 8B Open Model Sets New Standards for Safe and Efficient Vision-Language AI

This discussion summarizes our findings on the impact of data pruning on classification bias and underscores the promise of methods like MetriQ

Addressing Unfairness in Data Pruning for Deep Learning

Generative AI systems reinforce bias and stereotypes, harming minoritized users through omission, subordination, and stereotyping in everyday interactions.

AI Bias Isn't Always Loud—Sometimes Its Laissez-Faire

Discover Direct Nash Optimization (DNO) – a stable, scalable approach for LLM training that outperforms GPT-4 and Mistral on AlpacaEval 2.0. Learn more here.

What Does the Future of AI Model Training Hold?

Jamba’s Transformer-Mamba fusion enables long-context processing with reduced memory usage and superior throughput.

How Jamba Combines Transformers and Mamba to Build Smarter Language Models

Enhancing movie script summarization with scene saliency detection for better content selection and efficient storytelling.

What Makes a Scene Important? This AI Knows

Explore a novel AI framework for concept interoperability, using self-supervised learning and prototypes to ensure concept stability across domains.

Why Our Prototype-based AI Beats Traditional Concept Learning Approaches

We propose a fast learning algorithm for quotient PDFA to model LLMs under constrained generation and validate its efficiency and real-world utility.

Zero Isn’t a Problem, It’s a Shortcut: Rethinking PDFA Learning

MaCoDE turns masked language modeling into a powerful method for synthetic tabular data generation with high ML utility and privacy control.

Can BERT Generate Tables? Exploring MaCoDE’s Approach to Synthetic Data

Efficient and effective: A text-based LLM outperforms traditional methods in speech-to-text retrieval and translation tasks.

PVML

http://www.PVML.com

11-50 emps

Since 2022

PVML's stories on HackerNoon

The Doe Discuss How Tech and Social Media Has Changed Journalism

Hateful Meme Detection: Leveraging PVLMs for Zero-shot VQA Probing

Zero-Shot Visual Question Answering with PVLMs

Introducing PMLesson, Dynamic Product Management Interview Prep

DALL·E Research Paper: High Quality Image Generation Without Using Any of the Training Labels

A Developer's Guide to Building Next-Gen Smart Wallets With ERC-4337 — Part 2: Bundlers

Can Sparse Spectral Training Make AI More Accessible?

Toto: Time Series Optimized Transformer for Observability

Why Product Teams Risk Surrendering Judgment to Algorithms

AI Models Can't Be Trusted in High-Stakes Simulations Just Yet

Cut 90% of Fine-Tuning Cost—Still Beat Baselines on Text and Vision Benchmarks

Homogeneity and Normality: How to Test Your Experimental Data

How to Use TensorFlow Profiler to Optimize Model Performance

Reducing Blockchain Fees with R2-TFRM and On-Chain Randomness

How an 8B Open Model Sets New Standards for Safe and Efficient Vision-Language AI

Addressing Unfairness in Data Pruning for Deep Learning

AI Bias Isn't Always Loud—Sometimes Its Laissez-Faire

What Does the Future of AI Model Training Hold?

How Jamba Combines Transformers and Mamba to Build Smarter Language Models

What Makes a Scene Important? This AI Knows

Why Our Prototype-based AI Beats Traditional Concept Learning Approaches

Zero Isn’t a Problem, It’s a Shortcut: Rethinking PDFA Learning

Can BERT Generate Tables? Exploring MaCoDE’s Approach to Synthetic Data

The Minimalist’s Guide to Speech-to-Text: Big Wins with Little Data

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