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Generative AI has made remarkable progress in addressing various design challenges. One prominent area where generative AI could bring significant value is in engineering design. In particular, selecting an optimal set of components and their interfaces to create a mechanical system that meets design requirements is one of the most challenging and time-consuming tasks for engineers. This configuration design task is inherently challenging due to its categorical nature, multiple design requirements a solution must satisfy, and the reliance on physics simulations for evaluating potential solutions. These characteristics entail solving a combinatorial optimization problem with multiple constraints involving black-box functions. To address this challenge, we propose a generative AI model to predict the optimal combination of components and interfaces for a given design problem. To demonstrate our approach, we solve a gear train synthesis problem by first creating a synthetic dataset using a grammar, a parts catalogue, and a physics simulator. We then train a Transformer using this dataset, named *GearFormer*, which can not only generate quality solutions on its own, but also can augment search methods such as an evolutionary algorithm and Monte Carlo tree search. We show that GearFormer outperforms such search methods on their own in terms of satisfying the specified design requirements with an orders of magnitude faster generation time. Additionally, we showcase the benefit of hybrid methods that leverage both GearFormer and search methods, which further improve the quality of the solutions.

AAAI 2025

Deep Generative Model for Mechanical System Configuration Design

applications

poster

We are pleased to announce the Thirty-Ninth AAAI Conference on Artificial Intelligence (AAAI-25), which will be held in Philadelphia, Pennsylvania at the Pennsylvania Convention Center from February 25 to March 4, 2025.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-25 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.

### [Invited Speakers](https://aaai.org/conference/aaai/aaai-25/aaai-25-invited-speakers/)

Register [here](https://aaai.org/conference/aaai/aaai-25/registration/)

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-25 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.



Traditional multi-agent path finding (MAPF) methods try to compute entire start-goal paths which are collision free. However, computing an entire path can take too long for MAPF systems where agents need to replan fast. Methods that address this typically employ a ``windowed" approach and only try to find collision free paths for a small windowed timestep horizon. This adaptation comes at the cost of incompleteness; all current windowed approaches can become stuck in deadlock or livelock. Our main contribution is to introduce our framework, WinC-MAPF, for Windowed MAPF that enables completeness. Our framework uses heuristic update insights from single-agent real-time heuristic search algorithms as well as agent independence ideas from MAPF algorithms. We also develop Single-Step CBS (SS-CBS), an instantiation of this framework using a novel modification to CBS.
We show how SS-CBS, which only plans a single step and updates heuristics, can effectively solve tough scenarios where existing windowed approaches fail.

Windowed MAPF with Completeness Guarantees

Adversarial patches pose a significant threat to computer vision models' integrity, decreasing the accuracy of various tasks, including object detection (OD).
Most existing OD defenses exhibit a trade-off between enhancing the model's adversarial robustness and maintaining its performance on benign images. 
We propose KDAT (knowledge distillation with adversarial tuning), a novel mechanism that enhances the robustness of an OD model without compromising its performance on benign images or its inference time.
Our method combines the knowledge distillation (KD) technique with the adversarial tuning concept to teach the model to match the predictions of adversarial images with those of their corresponding benign ones.
To match these predictions, we designed four unique loss components, allowing the student model to effectively distill the knowledge of different features from various parts of the teacher model.
Our extensive evaluation on the COCO and INRIA datasets demonstrates KDAT's ability to improve the performance of Faster R-CNN and DETR on benign images by 2-4 mAP% and adversarial examples by 10-15 mAP%, outperforming other state-of-the-art (SOTA) defenses.
Furthermore, our additional physical evaluation on the Superstore dataset demonstrates KDAT's SOTA adversarial robustness against printed patches (improvement of 22 mAP% compared to the undefended model).

KDAT: Inherent Adversarial Robustness via Knowledge Distillation with Adversarial Tuning for Object Detection Models

Large language models (LLMs) based on transformer architecture have shown outstanding performance across numerous real-world tasks. However, the autoregressive nature of these models makes the inference process slow and costly. 
Speculative decoding has emerged as a promising solution, leveraging a smaller auxiliary model to draft future tokens, which are then validated simultaneously by the larger model, achieving a speed-up of $1$-$2\times$. Although speculative decoding matches the same distribution as multinomial sampling, multinomial sampling itself is prone to suboptimal outputs, where as beam sampling is widely recognized for producing higher-quality results by maintaining multiple candidate sequences at each step.
This paper explores the novel integration of speculative decoding with beam sampling. However, there are four key challenges: (1) how to generate multiple sequences from the larger model's distribution given drafts sequences from the small model; (2) how to dynamically optimize the number of beams to balance efficiency and accuracy; (3) how to efficiently verify the multiple drafts in parallel; and (4) how to address the extra memory costs inherent in beam sampling.
To address these challenges, we propose dynamic-width speculative beam decoding (\method{}). Specifically, we first introduce a novel draft and verification scheme that generates multiple sequences following the large model's distribution based on beam sampling trajectories from the small model. Then, we introduce an adaptive mechanism to dynamically tune the number of beams based on the context, optimizing efficiency and effectiveness. Besides, we extend tree-based parallel verification to handle multiple trees simultaneously, accelerating the verification process. Finally, we illustrate a simple modification to our algorithm to mitigate the memory overhead of beam sampling.
Experimental results show that our approach achieves a $1.5$-$1.9\times$ speed-up and $1.8$-$2.5\times$ lower energy consumption compared to beam sampling, with no loss in downstream performance. Moreover, it can produce significantly higher-quality outputs than speculative decoding, while maintaining similar time, memory, and energy costs. In summary, our method offers a more efficient and effective inference process for LLMs.

Dynamic-Width Speculative Beam Decoding for LLM Inference

Large Vision-Language Models (VLMs), possessing millions or billions of parameters, typically require large text and image datasets for effective fine-tuning. However, collecting data from various sites, especially in healthcare, is challenging due to strict privacy regulations. An alternative is to fine-tune these foundation models on end-user devices, such as in medical clinics and hospitals, without sending data to a server. These local clients typically have limited computing power and small datasets, which are not enough for fully fine-tuning large VLMs on their own. A naive solution to these scenarios is to leverage parameter-efficient fine-tuning (PEFT) strategies such as adapters and apply federated learning (FL) algorithms to combine the learned adapter weights, thereby respecting the resource limitations and data privacy of the clients. However, this approach does not fully leverage the knowledge from multiple adapters trained on diverse data distributions and for diverse tasks. The adapters are adversely impacted by data heterogeneity and task heterogeneity across clients resulting in sub-optimal convergence. To this end, we propose a novel framework called FedPIA that improves upon the naive combinations of FL and PEFT by introducing Permutation and Integration of the local Adapters in the server and global Adapters in the clients exploiting Wasserstein barycenters for improved blending of client-specific and client-agnostic knowledge. This layerwise permutation helps to bridge the gap in the parameter space of local and global adapters before integration. We conduct over 2000 client-level experiments utilizing 48 medical image datasets across five different medical vision-language FL task settings encompassing visual question answering as well as image and report-based multi-label disease detection. Our experiments involving diverse client settings, ten different modalities, and two VLM backbones demonstrate that FedPIA consistently outperforms the state-of-the-art PEFT-FL baselines.

FedPIA – Permuting and Integrating Adapters Leveraging Wasserstein Barycenters for Finetuning Foundation Models in Multi-Modal Federated Learning

Recent advancements in self-supervised denoising have made it possible to train models without needing a large amount of noisy-clean image pairs. A significant development in this area is the use of blind-spot networks (BSNs) which use a single noisy image as a training pair, masking some input information to prevent noise transmission to the network output. Researchers have shown that BSNs are capable of reconstructing clean pixels from various types of independent pixel-wise degradations, such as synthetic additive white Gaussian noise (AWGN). However, unlike synthetic noise, real noise often contains highly correlated components that can induce noise transmission and reduce the performance of BSNs. To address the spatial correlation of real noise, we propose the Adjacent Pixel Replacer (APR), which decorrelates noise without a downsampling process that is widely adopted in previous research. The dissimilarity between our APR-generated pair operates as relatively different noise components during training. Hence, it enables the BSN to block noise transmission while utilizing clean information effectively. As a result, BSN can refer to more dense information to reconstruct the corresponding center pixel. We also propose Recharged Distillation (RD) to enhance high-frequency textures without additional network modifications. This method refines clean information selectively from recharged noisy pixels during distillation. Extensive experimental results demonstrate that our proposed method outperforms the existing state-of-the-art self-supervised denoising methods in real sRGB space.

APR-RD: Complemental Two Steps for Self-Supervised Real Image Denoising

The scalability of instructable agents in robotics or gaming is often hindered by limited data that pairs instructions with agent trajectories. However, large datasets of unannotated trajectories containing sequences of various agent behaviour (play trajectories) are often available. In a semi-supervised setup, we explore methods to extract labelled segments from play trajectories. The goal is to augment a small annotated dataset of instruction-trajectory pairs to improve the performance of an instruction-following policy trained downstream via imitation learning. Utilising recent video segmentation models, we extract labelled segments assuming little variation in segment length. To address the constraint of segment length, we propose Play Segmentation (PS), a probabilistic model that finds maximum likely segmentations of extended subsegments, while only being trained on single instruction segments. Our results in a game environment and simulated robotic gripper setting underscore the importance of segmentation; randomly sampled segments diminish performance, while incorporating relabelled groundtruth segments improves policy performance. Adding labelled segments from PS improves policy performance to the level of a policy trained on twice the amount of labelled data.

Data Augmentation for Instruction Following Policies via Trajectory Segmentation

Portable Document Format (PDF) files are dominantly used for storing and disseminating scientific research, legal documents, and tax information. LaTeX is a popular application for creating PDF documents. Despite its advantages, LaTeX is not WYSWYG---what you see is what you get, i.e., the LaTeX source and rendered PDF images look drastically different, especially for formulae and tables. This gap makes it hard to modify or export LaTeX sources for formulae and tables from PDF images, and existing work is still limited. First, prior work generates LaTeX sources in a single iteration and struggles with complex LaTeX formulae. Second, existing work mainly recognizes and extracts LaTeX sources for formulae; and is incapable or ineffective for tables. This paper proposes LATTE, the first iterative refinement framework for LaTeX recognition. Specifically, we propose delta-view as feedback, which compares and pinpoints the differences between a pair of rendered images of the extracted LaTeX source and the expected correct image. Such delta-view feedback enables our fault localization model to localize the faulty parts of the incorrect recognition more accurately and enables our LaTeX refinement model to repair the incorrect extraction more accurately. LATTE improves the LaTeX source extraction accuracy of both LaTeX formulae and tables, outperforming existing techniques as well as GPT-4V by at least 7.07% of exact match, with a success refinement rate of 46.08% (formula) and 25.51% (table).

LATTE: Improving Latex Recognition for Tables and Formulae with Iterative Refinement

Catastrophic forgetting is when a neural network loses previously learnt information after learning a new task sequentially. If we avoid catastrophic forgetting, it would reduce the resources necessary to update neural networks. Recently, Kolmogorov–Arnold Networks (KAN) gained the community's attention as preliminary experiments suggest KANs avoid catastrophic forgetting (Liu et al. 2024). KANs replace neural network edges with learnable B-splines and sum incoming edges in nodes. Proponents of KANs argue they can avoid forgetting, are more accurate, are interpretable, and use fewer parameters. Our work investigates the claims that KANs avoid catastrophic forgetting, finding that they fail to do so on more complex datasets containing features that overlap between tasks. We give a simple explanation as to why and how KANs catastrophically forget. Motivated by evidence suggesting KANs have improved accuracy on symbolic regression datasets, we augment KANs in the same ways as MLPs to perform continual learning tasks, and we make special accommodations to support some of KANs' peculiarities. Our experiments found that unmodified KANs often forget more than MLPs, but KANs can be better than MLPs when combined with continual learning strategies. We aim to report our findings to the continual learning research community and highlight some of the current shortcomings and strengths associated with KANs for continual learning.

Kolmogorov-Arnold Networks Still Catastrophically Forget but Differently from MLP

Simple random negative sampling is a technique used to enhance decision-making in sequential models with numerous potential negative instances, like recommender systems. However, it ignores the patterns that can be discovered in complex sequences to select the most informative negative samples. In this paper, we address this challenge by introducing a Neighborhood-Aware Negative Sampling (NANS) technique in the context of student knowledge modeling (KM) and behavior modeling (BM). In the education domain, KM quantifies student knowledge based on past performance, while BM focuses on behaviors like student preferences of questions. With the vast number of problems to choose from and the intricate relationship between student knowledge and behavior, selecting the proper negative samples becomes a notable challenge in this problem. NANS, along with our proposed multi-objective, multi-task sequential model for KM and BM, NANS-KoBeM frames the simultaneous modeling of student knowledge and question selection as a multi-task learning problem with dual objectives: predicting students’ performance and their question selections.

Neighborhood-Aware Negative Sampling for Student Knowledge and Behavior Modeling

The problem of federated learning (FL) where users are distributed and partitioned into clusters has been addressed through the framework of clustered federated learning (CFL). However, when users are unwilling to share their cluster identities due to privacy concerns, CFL’s training becomes difficult. To address these issues, we introduce an innovative Efficient and Robust Secure Aggregation scheme for CFL, dubbed EBS-CFL. The proposed method supports training CFL while maintaining user cluster identity confidentiality. It detects potential poisoning attacks without compromising individual client gradients by discarding negatively correlated gradients and aggregating positively correlated ones using a weighted approach. The server also authenticates correct gradient encoding by clients. 
Client-side overhead is $O(ml + m^2)$ for communication and $O(m^2l)$ for computation. When $m = 1$, computational efficiency is at least $\log{n}$ times better than other methods, where $n$ is the number of clients, $m$ is the number of cluster identities, and $l$ is the gradient size.
Our method's theoretical efficiency and security are validated through comprehensive analysis and extensive experiments.

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