Categories: FAANG

Combining Compressions for Multiplicative Size Scaling on Natural Language Tasks

Quantization, knowledge distillation, and magnitude pruning are among the most popular methods for neural network compression in NLP. Independently, these methods reduce model size and can accelerate inference, but their relative benefit and combinatorial inter- actions have not been rigorously studied. For each of the eight possible subsets of these techniques, we compare accuracy vs. model size tradeoffs across six BERT architecture sizes and eight GLUE tasks. We find that quantization and distillation consistently provide greater benefit than pruning. Surprisingly, except for the pair of…
AI Generated Robotic Content

Recent Posts

Pirate VFX Breakdown | Made almost exclusively with SDXL and Wan!

In the past weeks, I've been tweaking Wan to get really good at video inpainting.…

15 hours ago

Try Deep Think in the Gemini app

Deep Think utilizes extended, parallel thinking and novel reinforcement learning techniques for significantly improved problem-solving.

15 hours ago

Introducing Amazon Bedrock AgentCore Browser Tool

At AWS Summit New York City 2025, Amazon Web Services (AWS) announced the preview of…

15 hours ago

New vision model from Cohere runs on two GPUs, beats top-tier VLMs on visual tasks

Cohere's Command A Vision can read graphs and PDFs to make enterprise research richer and…

16 hours ago

Anthropic Revokes OpenAI’s Access to Claude

OpenAI lost access to the Claude API this week after Anthropic claimed the company was…

16 hours ago

New AI tool learns to read medical images with far less data

A new artificial intelligence (AI) tool could make it much easier—and cheaper—for doctors and researchers…

16 hours ago