Tagged articles

temperature scaling

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Oct 21, 2025 · Artificial Intelligence

Boost LLM Originality: Master Temperature Scaling & Top‑K Sampling

This tutorial revisits a simple text‑generation function, explains how temperature scaling and top‑K sampling reshape token probability distributions, demonstrates their effects with PyTorch code and visualizations, and shows how to integrate both techniques into an improved generation routine for more diverse and human‑like outputs.

LLMPyTorchtemperature scaling

0 likes · 13 min read

Boost LLM Originality: Master Temperature Scaling & Top‑K Sampling

Subtle Storm

Feb 23, 2025 · Artificial Intelligence

What Is Knowledge Distillation and How Does It Compress Deep Models?

Knowledge Distillation transfers the soft output probabilities of a high‑capacity teacher network to a compact student model, enabling significant reductions in parameters and compute while preserving accuracy, and is widely used for model compression, inference acceleration, and transfer learning across resource‑constrained devices.

deep learningknowledge distillationmodel compression

0 likes · 7 min read

What Is Knowledge Distillation and How Does It Compress Deep Models?

DataFunSummit

Oct 29, 2021 · Artificial Intelligence

Contrastive Learning Perspectives on Retrieval and Ranking Models in Recommendation Systems

This talk explains contrastive learning fundamentals, typical image‑domain models such as SimCLR, MoCo and SwAV, and shows how their principles—positive/negative sample construction, encoder design, loss functions, alignment and uniformity—can be applied to improve dual‑tower retrieval and ranking models, embedding normalization, temperature scaling, and graph‑based recommender systems.

InfoNCERecommendation Systemscontrastive learning

0 likes · 40 min read

Contrastive Learning Perspectives on Retrieval and Ranking Models in Recommendation Systems

DataFunTalk

Oct 26, 2021 · Artificial Intelligence

Contrastive Learning Perspective on Retrieval and Reranking Models in Recommendation Systems

This article explains how contrastive learning, originally popular in computer‑vision, can be interpreted and applied to recommendation‑system recall and coarse‑ranking models, covering its theoretical roots, typical architectures like SimCLR, MoCo and SwAV, and practical tricks such as in‑batch negatives, embedding normalization, temperature scaling, and graph‑based extensions.

Recommendation SystemsSelf-supervised Learningcontrastive learning

0 likes · 40 min read

Contrastive Learning Perspective on Retrieval and Reranking Models in Recommendation Systems