'I don't know!': Teaching neural networks to abstain with the HALO-Loss. [R]

This r/MachineLearning post discusses research on training neural networks to recognize and express uncertainty by introducing a novel HALO-Loss function that gives models an explicit "abstention" option during inference. Rather than forcing a network to always output a prediction, the approach allows it to withhold a decision on ambiguous or out-of-distribution samples — effectively learning to say "I don't know." The discussion likely covers the loss function's design, its impact on accuracy-coverage trade-offs, and comparisons to standard classification loss functions.

'There's a new generation of empirical deep learning researchers, hacking away at whatever seems trendy, blowing with the wind' [D]
I scaled a pure Spiking Neural Network (SNN) to 1.088B parameters from scratch. Ran out of budget, but here is what I found [R]
Thinking Deeper, Not Longer: Depth-Recurrent Transformers for Compositional Generalization [R]
Implementation details of Backpropagation in Siamese networks. [D]
Trained a Qwen2.5-0.5B-Instruct bf16 model on Reddit post summarization task with GRPO [P]

Source: r/MachineLearning | 2026-04-14

Related