It possesses the advantages of AED-based model's accuracy, NAR model's efficiency, and explicit customization capacity of superior performance.
Particularly, we show that the standard transformer block can be derived from alternating optimization on complementary parts of this objective: the multi-head self-attention operator can be viewed as a gradient descent step to compress the token sets by minimizing their lossy coding rate, and the subsequent multi-layer perceptron can be viewed as attempting to sparsify the representation of the tokens.
First, we train a degradation-aware prompt extractor, which can generate accurate soft and hard semantic prompts even under strong degradation.
Alignment has become a critical step for instruction-tuned Large Language Models (LLMs) to become helpful assistants.
We present a method to create interpretable concept sliders that enable precise control over attributes in image generations from diffusion models.
Zero-shot 6D object pose estimation involves the detection of novel objects with their 6D poses in cluttered scenes, presenting significant challenges for model generalizability.
We present XPhoneBERT, the first multilingual model pre-trained to learn phoneme representations for the downstream text-to-speech (TTS) task.