We conduct an empirical investigation to show fine-tuning will corrupt the context-aware ability of pre-trained CLIP features. To solve this problem, we propose Context-Aware Robust Fine-tuning (CAR-FT). CAR-FT regularizes the model during fine-tuning to capture the context information. ...
batch effect). (2) During the fine-tuning stage, a supervised model is appended to the pre-trained CODE-AE and trained based on the deconfounded and aligned embedding of cell lines using labelled cell-line drug response data. (3) During the inference stage, the deconfounded...
and NTK-Aware RoPE (NTK) (Lo-calLLaMA, 2023b;a), are adaptations of Rotary Positional Encodings (RoPE) (Su et al., 2022). These scaled positional encodings necessitate fewer finetuning steps compared to the original RoPE, and their training costs can be further reduced via methods such as...
The three main characteristics of this system are scale-space representation using pyramidal descriptors; hierarchical, localized, and robust indexing and classification of the feature models using a distributed associative memory; and adaptive saccade and zoom strategies guided by saliency to locate and ...
The training objective in this case becomes L = −log pΘ(Txi |hAD), which is similar to training a story completion objective [53] by finetuning GPT on text-only movie AD data but with a few additional special tokens. This text-only movie AD pretraining is also related to [27], ...
generative AI models can be trained or fine-tuned to produce more comprehensive and context-aware outputs. This approach can lead to the generation of synthetic data, which is particularly useful in cases where real-world medical data is scarce or difficult to obtain. The generated data can be...
Prefix-Tuning: Optimizing Continuous Prompts for Generation, ACL 2021. Paper P-Tuning: Prompt Tuning Can Be Comparable to Fine-tuning Across Scales and Tasks, ACL 2022. Paper P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks, Arxiv 2022. Paper Pre...
Given the increasing cost of long-context finetuning for 70B models, we did not find many open-source 70B baselines. We compare our training-free method against the robust 70B baseline, Longlora (Chen et al., 2023c), which employs LoRA-based (Hu et al., 2021) efficient tuning based on...
Given our desire to develop a model robust to both low-input (i.e. single cell / ST) and high-input (i.e. bulk RNA) samples for deconvolution, we randomly selected a value from 1 to 10,000 with uniform probability. (2) The number of unique cell types (N) in a mixture is ...
. Thirdly, Riboformer is not designed to handle rare events like ribosomal frameshifting, due to the limited number of training samples. To tackle these specific situations, transfer learning approaches could be explored, which allows for initial training on one task and subsequent fine-tuning ...