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...
The improved results obtained by unfreezing the weights of more layers than solely the last fully connected layer (considered for EgoTerrainNets fine-tuning) was likely due to the fewer number of classes in the binary classification approach (vs 5 for EgoTerrainNet-Outdoor), and thus, the ...
The improved results obtained by unfreezing the weights of more layers than solely the last fully connected layer (considered for EgoTerrainNets fine-tuning) was likely due to the fewer number of classes in the binary classification approach (vs 5 for EgoTerrainNet-Outdoor), and thus, the ...
Naive chunking strategies often result in poor outputs for synthetic data generation and thereby finetuning of language models. Context aware chunking can result in reduced hallucinations involving complex document structures. This can facilitate seamless integration across various departments within an organiz...
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...
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...
, 2013). Combined, these domain-independent sources of rich semantic information provide a robust initialization for the embedding layer to better accommodate unseen words (i.e., never seen during training), which greatly facilitates zero-shot slot filling. Step two fine-tunes the semantically rich...
This means that the error gradient of the model will flow backward from the output of each time step, allowing multiple fine-tuning of BERT. 3.2 Word-Level Argument Recognition Model Following the previous section, we will perform argument boundary recognition if the event argument positioning ...
Therefore, designers should be involved in the commissioning and first operation period of the building to provide some fine tuning of specific features and instruct occupants on the building’s proper functions, as energy performance, comfort, and building operation–both passive and active—are more...