Research

Studying the Impact of Magnitude Pruning on Contrastive Learning Methods

Cohere

We study the impact of different pruning techniques on the representation learned by deep neural networks trained with contrastive loss functions.

Visit Site

Research Cohere

ResearchContrastive Policy Gradient: Aligning LLMs on sequence-level scores in a supervised-friendly fashionCohere ResearchTo Code, or Not To Code? Exploring Impact of Code in Pre-trainingCohere ResearchSimMerge: Learning to Select Merge Operators from Similarity SignalsCohere ResearchLifting the Veil on Hyper-parameters for Value-based Deep Reinforcement LearningCohere ResearchAssociative Memory Augmented Asynchronous Spatiotemporal Representation Learning for Event-basedCohere ResearchNear-Optimal Distributionally Robust Reinforcement Learning with General NormsCohere BlogsHow to implement AI training for employeesZapier NewsLessons Swift Designer Chris Lattner Has Learned about LeadershipThenewstack BlogsSummer Data Engineering RoadmapMotherduck BlogsReal-Time Computational Physics with Wafer-Scale Processing [updated]Cerebras BlogsMeta AI Video Generator's ImpactHeygen BlogsHow To Create and Use Avatars in eLearningHeygen BlogsFrom Hawking to Siri: The Evolution of Speech SynthesisDeepgram ResourcesConfiguration and settingsOpentelemetry BlogsHow Sky Italia Digitizes Learning Paths to Accelerate New Product Launches by 4xSynthesia BlogsHow EPOS achieved a 90% learning completition rate with AI video.Synthesia BlogsHow Mondelez Accelerates Supply Chain Training Across 150+ Global Manufacturing PlantsSynthesia NewsTraining: Hard Work Pays off for Data/AI ProsThenewstack NewsBreakdown: The Kubernetes-Run AI Video Generation Pipeline for NIUS.TVThenewstack BlogsCerebras Architecture Deep Dive: First Look Inside the HW/SW Co-Design for Deep Learning [Updated]Cerebras