Modality-agnostic decoders leverage modality-invariant representations in human subjects' brain activity to predict stimuli irrespective of their modality (image, text, mental imagery).

Microsoft launches three in-house AI models for transcription, voice, and image generation, challenging OpenAI and Google with lower-cost systems.

Abstract: Change detection plays a vital role in numerous real-world domains, aiming to accurately identify regions that have changed between two temporally distinct images. Capturing the complex ...

The implementation is intentionally explicit and educational, avoiding high-level abstractions where possible. . ├── config.py # Central configuration file defining model hyperparameters, training ...

We cross-validated four pretrained Bidirectional Encoder Representations from Transformers (BERT)–based models—BERT, BioBERT, ClinicalBERT, and MedBERT—by fine-tuning them on 90% of 3,261 sentences ...

I want to train pretrain a sentence transformer using TSDAE. We have previously used all-MiniLM-L6-v2 as a checkpoint where we finetuned with MultipleNegativeRankingLoss with the main downstream task ...