Chau Pham

selected publications

1. NeurIPS

   Enhancing Feature Diversity Boosts Channel-Adaptive Vision Transformers

   Chau Pham, and Bryan A. Plummer

   In Advances in Neural Information Processing Systems (NeurIPS) 2024

   Abstract PDF Bibtex Code

   Multi-Channel Imaging (MCI) contains an array of challenges for encoding useful feature representations not present in traditional images. For example, images from two different satellites may both contain RGB channels, but the remaining channels can be different for each imaging source. Thus, MCI models must support a variety of channel configurations at test time. Recent work has extended traditional visual encoders for MCI, such as Vision Transformers (ViT), by supplementing pixel information with an encoding representing the channel configuration. However, these methods treat each channel equally, i.e., they do not consider the unique properties of each channel type, which can result in needless and potentially harmful redundancies in the learned features. For example, if RGB channels are always present, the other channels can focus on extracting information that cannot be captured by the RGB channels. To this end, we propose DiChaViT, which aims to enhance the diversity in the learned features of MCI-ViT models. This is achieved through a novel channel sampling strategy that encourages the selection of more distinct channel sets for training. Additionally, we employ regularization and initialization techniques to increase the likelihood that new information is learned from each channel. Many of our improvements are architecture agnostic and can be incorporated into new architectures as they are developed. Experiments on both satellite and cell microscopy datasets, CHAMMI, JUMP-CP, and So2Sat, report DiChaViT yields a 1.5 - 5.0% gain over the state-of-the-art. Our code is publicly available at https://github.com/chaudatascience/diverse_channel_vit.

   @InProceedings{phamDiChaVit2024,
   author = {Chau Pham and Bryan A. Plummer},
   title = {Enhancing Feature Diversity Boosts Channel-Adaptive Vision Transformers},
   booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
   year = {2024}}
2. ICLR

   Let Models Speak Ciphers: Multiagent Debate through Embeddings

   Chau Pham*, Boyi Liu*, Yingxiang Yang, Zhengyu Chen, Tianyi Liu, Jianbo Yuan, Bryan A. Plummer, Zhaoran Wang, and Hongxia Yang

   In International Conference on Learning Representations (ICLR) 2024

   Abstract PDF Bibtex Code

   Discussion and debate among Large Language Models (LLMs) have gained considerable attention due to their potential to enhance the reasoning ability of LLMs. Although natural language is an obvious choice for communication due to LLM’s language understanding capability, the token sampling step needed when generating natural language poses a potential risk of information loss, as it uses only one token to represent the model’s belief across the entire vocabulary. In this paper, we introduce a communication regime named CIPHER (Communicative Inter-Model Protocol Through Embedding Representation) to address this issue. Specifically, we remove the token sampling step from LLMs and let them communicate their beliefs across the vocabulary through the expectation of the raw transformer output embeddings. Remarkably, by deviating from natural language, CIPHER offers an advantage of encoding a broader spectrum of information without any modification to the model weights, outperforming the state-of-the-art LLM debate methods using natural language by 0.5-5.0% across five reasoning tasks and multiple open-source LLMs of varying sizes. This showcases the superiority and robustness of embeddings as an alternative "language" for communication among LLMs. We anticipate that CIPHER will inspire further exploration for the design of interactions within LLM agent systems, offering a new direction that could significantly influence future developments in the field.

   @InProceedings{phamCIPHER2024,
   author={Chau Pham and Boyi Liu and Yingxiang Yang and Zhengyu Chen and Tianyi Liu and Jianbo Yuan and Bryan A. Plummer and Zhaoran Wang and Hongxia Yang},
   title={Let Models Speak Ciphers: Multiagent Debate through Embeddings},
   booktitle={International Conference on Learning Representations (ICLR)},
   year={2024}}
3. WACV

   MixtureGrowth: Growing Neural Networks by Recombining Learned Parameters

   Chau Pham*, Piotr Teterwak*, Soren Nelson*, and Bryan A. Plummer

   In IEEE Winter Conference on Applications of Computer Vision (WACV) 2024

   Abstract PDF Bibtex Code

   Most deep neural networks are trained under fixed network architectures and require retraining when the architecture changes. If expanding the network’s size is needed, it is necessary to retrain from scratch, which is expensive. To avoid this, one can grow from a small network by adding random weights over time to gradually achieve the target network size. However, this naive approach falls short in practice as it brings too much noise to the growing process. Prior work tackled this issue by leveraging the already learned weights and training data for generating new weights through conducting a computationally expensive analysis step. In this paper, we introduce MixtureGrowth, a new approach to growing networks that circumvents the initialization overhead in prior work. Before growing, each layer in our model is generated with a linear combination of parameter templates. Newly grown layer weights are generated by using a new linear combination of existing templates for a layer. On one hand, these templates are already trained for the task, providing a strong initialization. On the other, the new coefficients provide flexibility for the added layer weights to learn something new. We show that our approach boosts top-1 accuracy over the state-of-the-art by 2-2.5% on CIFAR-100 and ImageNet datasets, while achieving comparable performance with fewer FLOPs to a larger network trained from scratch. Code is available at https://github.com/chaudatascience/mixturegrowth.

   @InProceedings{phamMixtureGrowth2024,
   author={Chau Pham and Piotr Teterwak and Soren Nelson and Bryan A. Plummer},
   title={MixtureGrowth: Growing Neural Networks by Recombining Learned Parameters},
   booktitle={IEEE Winter Conference on Applications of Computer Vision (WACV)},
   year={2024}}
4. NeurIPS

   CHAMMI: A benchmark for channel-adaptive models in microscopy imaging

   Zitong Chen*, Chau Pham*, Siqi Wang, Michael Doron, Nikita Moshkov, Bryan A. Plummer, and Juan C. Caicedo

   In Advances in Neural Information Processing Systems (NeurIPS) 2023

   Abstract PDF Bibtex Code Data

   Most neural networks assume that input images have a fixed number of channels (three for RGB images). However, there are many settings where the number of channels may vary, such as microscopy images where the number of channels changes depending on instruments and experimental goals. Yet, there has not been a systemic attempt to create and evaluate neural networks that are invariant to the number and type of channels. As a result, trained models remain specific to individual studies and are hardly reusable for other microscopy settings. In this paper, we present a benchmark for investigating channel-adaptive models in microscopy imaging, which consists of 1) a dataset of varied-channel single-cell images, and 2) a biologically relevant evaluation framework. In addition, we adapted several existing techniques to create channel-adaptive models and compared their performance on this benchmark to fixed-channel, baseline models. We find that channel-adaptive models can generalize better to out-of-domain tasks and can be computationally efficient. We contribute a curated dataset and an evaluation API to facilitate objective comparisons in future research and applications.

   @InProceedings{ChenCHAMMI2023,
   author={Zitong Chen and Chau Pham and Siqi Wang and Michael Doron and Nikita Moshkov and Bryan A. Plummer and Juan C Caicedo},
   title={CHAMMI: A benchmark for channel-adaptive models in microscopy imaging},
   booktitle={Advances in Neural Information Processing Systems (NeurIPS) Track on Datasets and Benchmarks},
   year={2023}}