Leveraging Priors via Diffusion Bridge for Time Series Generation

Time series generation is widely used in real-world applications such as simulation, data augmentation, and hypothesis test techniques. Recently, diffusion models have emerged as the de facto approach for time series generation, emphasizing diverse synthesis scenarios based on historical or correlated time series data streams. Since time series have unique characteristics, such as fixed time order and data scaling, standard Gaussian prior might be ill-suited for general time series generation. In this paper, we exploit the usage of diverse prior distributions for synthesis. Then, we propose TimeBridge, a framework that enables flexible synthesis by leveraging diffusion bridges to learn the transport between chosen prior and data distributions. Our model covers a wide range of scenarios in time series diffusion models, which leverages (i) data- and time-dependent priors for unconditional synthesis, and (ii) data-scale preserving synthesis with a constraint as a prior for conditional generation. Experimentally, our model achieves state-of-the-art performance in both unconditional and conditional time series generation tasks.

Designing Extremely Memory-Efficient CNNs for On-device Vision Tasks

In this paper, we introduce a memory-efficient CNN (convolutional neural network), which enables resource-constrained low-end embedded and IoT devices to perform on-device vision tasks, such as image classification and object detection, using extremely low memory, i.e., only 63 KB on ImageNet classification. Based on the bottleneck block of MobileNet, we propose three design principles that significantly curtail the peak memory usage of a CNN so that it can fit the limited KB memory of the low-end device. First, 'input segmentation' divides an input image into a set of patches, including the central patch overlapped with the others, reducing the size (and memory requirement) of a large input image. Second, 'patch tunneling' builds independent tunnel-like paths consisting of multiple bottleneck blocks per patch, penetrating through the entire model from an input patch to the last layer of the network, maintaining lightweight memory usage throughout the whole network. Lastly, 'bottleneck reordering' rearranges the execution order of convolution operations inside the bottleneck block such that the memory usage remains constant regardless of the size of the convolution output channels. The experiment result shows that the proposed network classifies ImageNet with extremely low memory (i.e., 63 KB) while achieving competitive top-1 accuracy (i.e., 61.58\%). To the best of our knowledge, the memory usage of the proposed network is far smaller than state-of-the-art memory-efficient networks, i.e., up to 89x and 3.1x smaller than MobileNet (i.e., 5.6 MB) and MCUNet (i.e., 196 KB), respectively.

Are Self-Attentions Effective for Time Series Forecasting?

Time series forecasting is crucial for applications across multiple domains and various scenarios. Although Transformer models have dramatically shifted the landscape of forecasting, their effectiveness remains debated. Recent findings have indicated that simpler linear models might outperform complex Transformer-based approaches, highlighting the potential for more streamlined architectures. In this paper, we shift focus from the overall architecture of the Transformer to the effectiveness of self-attentions for time series forecasting. To this end, we introduce a new architecture, Cross-Attention-only Time Series transformer (CATS), that rethinks the traditional Transformer framework by eliminating self-attention and leveraging cross-attention mechanisms instead. By establishing future horizon-dependent parameters as queries and enhanced parameter sharing, our model not only improves long-term forecasting accuracy but also reduces the number of parameters and memory usage. Extensive experiment across various datasets demonstrates that our model achieves superior performance with the lowest mean squared error and uses fewer parameters compared to existing models.

Can Large Language Models Replicate ITS Feedback on Open-Ended Math Questions?

Intelligent Tutoring Systems (ITSs) often contain an automated feedback component, which provides a predefined feedback message to students when they detect a predefined error. To such a feedback component, we often resort to template-based approaches. These approaches require significant effort from human experts to detect a limited number of possible student errors and provide corresponding feedback. This limitation is exemplified in open-ended math questions, where there can be a large number of different incorrect errors. In our work, we examine the capabilities of large language models (LLMs) to generate feedback for open-ended math questions, similar to that of an established ITS that uses a template-based approach. We fine-tune both open-source and proprietary LLMs on real student responses and corresponding ITS-provided feedback. We measure the quality of the generated feedback using text similarity metrics. We find that open-source and proprietary models both show promise in replicating the feedback they see during training, but do not generalize well to previously unseen student errors. These results suggest that despite being able to learn the formatting of feedback, LLMs are not able to fully understand mathematical errors made by students.

Math Multiple Choice Question Generation via Human-Large Language Model Collaboration

Multiple choice questions (MCQs) are a popular method for evaluating students' knowledge due to their efficiency in administration and grading. Crafting high-quality math MCQs is a labor-intensive process that requires educators to formulate precise stems and plausible distractors. Recent advances in large language models (LLMs) have sparked interest in automating MCQ creation, but challenges persist in ensuring mathematical accuracy and addressing student errors. This paper introduces a prototype tool designed to facilitate collaboration between LLMs and educators for streamlining the math MCQ generation process. We conduct a pilot study involving math educators to investigate how the tool can help them simplify the process of crafting high-quality math MCQs. We found that while LLMs can generate well-formulated question stems, their ability to generate distractors that capture common student errors and misconceptions is limited. Nevertheless, a human-AI collaboration has the potential to enhance the efficiency and effectiveness of MCQ generation.

Exploring Automated Distractor Generation for Math Multiple-choice Questions via Large Language Models

Multiple-choice questions (MCQs) are ubiquitous in almost all levels of education since they are easy to administer, grade, and are a reliable format in assessments and practices. One of the most important aspects of MCQs is the distractors, i.e., incorrect options that are designed to target common errors or misconceptions among real students. To date, the task of crafting high-quality distractors largely remains a labor and time-intensive process for teachers and learning content designers, which has limited scalability. In this work, we study the task of automated distractor generation in the domain of math MCQs and explore a wide variety of large language model (LLM)-based approaches, from in-context learning to fine-tuning. We conduct extensive experiments using a real-world math MCQ dataset and find that although LLMs can generate some mathematically valid distractors, they are less adept at anticipating common errors or misconceptions among real students.

Fundamental Benefit of Alternating Updates in Minimax Optimization

The Gradient Descent-Ascent (GDA) algorithm, designed to solve minimax optimization problems, takes the descent and ascent steps either simultaneously (Sim-GDA) or alternately (Alt-GDA). While Alt-GDA is commonly observed to converge faster, the performance gap between the two is not yet well understood theoretically, especially in terms of global convergence rates. To address this theory-practice gap, we present fine-grained convergence analyses of both algorithms for strongly-convex-strongly-concave and Lipschitz-gradient objectives. Our new iteration complexity upper bound of Alt-GDA is strictly smaller than the lower bound of Sim-GDA; i.e., Alt-GDA is provably faster. Moreover, we propose Alternating-Extrapolation GDA (Alex-GDA), a general algorithmic framework that subsumes Sim-GDA and Alt-GDA, for which the main idea is to alternately take gradients from extrapolations of the iterates. We show that Alex-GDA satisfies a smaller iteration complexity bound, identical to that of the Extra-gradient method, while requiring less gradient computations. We also prove that Alex-GDA enjoys linear convergence for bilinear problems, for which both Sim-GDA and Alt-GDA fail to converge at all.

Fair Sampling in Diffusion Models through Switching Mechanism

Diffusion models have shown their effectiveness in generation tasks by well-approximating the underlying probability distribution. However, diffusion models are known to suffer from an amplified inherent bias from the training data in terms of fairness. While the sampling process of diffusion models can be controlled by conditional guidance, previous works have attempted to find empirical guidance to achieve quantitative fairness. To address this limitation, we propose a fairness-aware sampling method called \textit{attribute switching} mechanism for diffusion models. Without additional training, the proposed sampling can obfuscate sensitive attributes in generated data without relying on classifiers. We mathematically prove and experimentally demonstrate the effectiveness of the proposed method on two key aspects: (i) the generation of fair data and (ii) the preservation of the utility of the generated data.

Attention Mechanism for Lithium-Ion Battery Lifespan Prediction: Temporal and Cyclic Attention

Accurately predicting the lifespan of lithium-ion batteries (LIBs) is pivotal for optimizing usage and preventing accidents. Previous studies in constructing prediction models often relied on inputs challenging to measure in real-time operations and failed to capture intra-cycle and inter-cycle data patterns, essential features for accurate predictions, comprehensively. In this study, we employ attention mechanisms (AM) to develop data-driven models for predicting LIB lifespan using easily measurable inputs such as voltage, current, temperature, and capacity data. The developed model integrates recurrent neural network (RNN) and convolutional neural network (CNN) components, featuring two types of attention mechanisms: temporal attention (TA) and cyclic attention (CA). The inclusion of TA aims to identify important time steps within each cycle by scoring the hidden states of the RNN, whereas CA strives to capture key features of inter-cycle correlations through self-attention (SA). This enhances model accuracy and elucidates critical features in the input data. To validate our method, we apply it to publicly available cycling data consisting of three batches of cycling modes. The calculated TA scores highlight the rest phase as a key characteristic distinguishing LIB data among different batches. Additionally, CA scores reveal variations in the importance of cycles across batches. By leveraging CA scores, we explore the potential to reduce the number of cycles in the input data. The single-head and multi-head attentions enable us to decrease the input dimension from 100 to 50 and 30 cycles, respectively.

Exploring Automated Distractor and Feedback Generation for Math Multiple-choice Questions via In-context Learning

Multiple-choice questions (MCQs) are ubiquitous in almost all levels of education since they are easy to administer, grade, and are a reliable format in both assessments and practices. An important aspect of MCQs is the distractors, i.e., incorrect options that are designed to target specific misconceptions or insufficient knowledge among students. To date, the task of crafting high-quality distractors has largely remained a labor-intensive process for teachers and learning content designers, which has limited scalability. In this work, we explore the task of automated distractor and corresponding feedback message generation in math MCQs using large language models. We establish a formulation of these two tasks and propose a simple, in-context learning-based solution. Moreover, we explore using two non-standard metrics to evaluate the quality of the generated distractors and feedback messages. We conduct extensive experiments on these tasks using a real-world MCQ dataset that contains student response information. Our findings suggest that there is a lot of room for improvement in automated distractor and feedback generation. We also outline several directions for future work