13 accepted papers
Designing vehicle exteriors requires repeated refinement of key proportions and viewpoints, a process traditionally reliant on manual sketching, which is often time-consuming and inefficient in early concept stages. To accelerate the design process, we are exploring the potential of utilizing AI for
While large language models (LLMs) have achieved remarkable performance across various natural language processing (NLP) tasks, LLMs exhibit a limited understanding of commonsense reasoning due to the necessity of implicit knowledge that is rarely expressed in text. Recently, retrieval-augmented lan
Parameter-efficient fine-tuning (PEFT) addresses the memory footprint issue of full fine-tuning by modifying only a subset of model parameters. However, on datasets exhibiting spurious correlations, we observed that PEFT slows down the model’s convergence on unbiased examples, while the convergence…
We present OuroMamba, the first data-free post-training quantization (DFQ) method for vision Mamba-based models (VMMs). We identify two key challenges in enabling DFQ for VMMs, (1) VMM's recurrent state transitions restricts the capturing of long-range interactions and leads to semantically weak syn…
Emotion context sensitivity—the ability to adjust emotional responses based on contexts—is a core component of human emotional intelligence. For example, being told, “You can come with me if you want,” may elicit joy if the destination is a mall, but provoke fear if the destination is a trap house.
In this paper, we introduce COCONUT to effectively guide the contextualization of structured commonsense knowledge based on largelanguage models. COCONUT employs a contextualized knowledge prompting scheme to gather high-quality contextualization examplesfrom a large language model. These examples a…
Chemical representation learning has gained increasing interest due to the limited availability of supervised data in fields such as drug and materials design. This interest particularly extends to chemical language representation learning, which involves pre-training Transformers on SMILES sequence…
We propose a text-guided variational image generation method to address the challenge of getting clean data for anomaly detection in industrial manufacturing. Our method utilizes text information about the target object learned from extensive text library documents to generate non-defective data ima…
Biases in the dataset often enable the model to achieve high performance on in-distribution data, while poorly performing on out-of-distribution data. To mitigate the detrimental effect of the bias on the networks, previous works have proposed debiasing methods that down-weight the biased examples i…
Computational inefficiency in transformers has been a long-standing challenge, hindering the deployment in resource-constrained or real-time applications. One promising approach to mitigate this limitation is to progressively remove less significant tokens, given that the sequence length strongly co…
Self-supervised pre-training has achieved remarkable success in extensive natural language processing tasks. Masked language modeling (MLM) has been widely used for pre-training effective bidirectional representations but comes at a substantial training cost. In this paper, we propose a novel concep…
Commonsense inference poses a unique challenge to reason and generate the physical, social, and causal conditions of a given event. Existing approaches to commonsense inference utilize commonsense transformers, which are large-scale language models that learn commonsense knowledge graphs. However, t…
Pre-trained language models have achieved remarkable successes in natural language processing tasks, coming at the cost of increasing model size. To address this issue, knowledge distillation (KD) has been widely applied to compress language models. However, typical KD approaches for language models…