11 accepted papers
Quantization-aware training is widely used for language model quantization in sub-4-bit precision, by training full-precision weights with gradients computed on the quantized model. The main bottleneck for this training approach is its slow convergence and plateauing of test performance, which gets …
The semi-supervised medical image segmentation with a few annotated data can provide significant help in robot-assisted surgery. This step plays a pivotal role in identification of pathological regions, more appropriate planning of surgical procedures, and so on. In this work, we develop an evidence
This paper develops an ensemble method for fine-tuning a language model to multiple datasets. Existing methods, such as quantized LoRA (QLoRA), are efficient when adapting to a single dataset. When training on multiple datasets of different tasks, a common setup in practice, it remains unclear how t…
This paper introduces an algorithm to select demonstration examples for in-context learning of a query set. Given a set of n examples, how can we quickly select k out of n to best serve as the conditioning for downstream inference? This problem has broad applications in prompt tuning and chain-of-th
The event camera, a bio-inspired asynchronous triggered camera, offers promising prospects for fusion with frame-based cameras owing to its low latency and high dynamic range. However, calibrating stereo vision systems that incorporate both event- and frame-based cameras remains a significant challe
We study the problem of fine-tuning a language model (LM) for a target task by optimally using the information from n auxiliary tasks. This problem has broad applications in NLP, such as targeted instruction tuning and data selection in chain-of-thought fine-tuning. The key challenge of this problem…
Graph neural networks are widely used tools for graph prediction tasks. Motivated by their empirical performance, prior works have developed generalization bounds for graph neural networks, which scale with graph structures in terms of the maximum degree. In this paper, we present generalization bou…
We consider the problem of traffic accident analysis on a road network based on road network connections and traffic volume. Previous works have designed various deep-learning methods using historical records to predict traffic accident occurrences. However, there is a lack of consensus on how accur…
Existing feature matching methods tend to extract feature descriptors by feeding down-sampled feature maps into a Transformer that is unable to extend feature scales, leading to false correspondences between small-size objects. This paper proposes MSFormer, which uses Transformers situated in differ…
We consider transfer learning approaches that fine-tune a pretrained deep neural network on a target task. We investigate generalization properties of fine-tuning to understand the problem of overfitting, which often happens in practice. Previous works have shown that constraining the distance from…
A widely used algorithm for transfer learning is fine-tuning, where a pre-trained model is fine-tuned on a target task with a small amount of labeled data. When the capacity of the pre-trained model is much larger than the size of the target data set, fine-tuning is prone to overfitting and "memoriz…