Association for the Advancement of Artificial Intelligence: AAAI Publications
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Data Augmentation for Instruction Following Policies via Trajectory Segmentation
No full textThe 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. Assuming little variation in segment length, recent video segmentation methods can effectively extract labelled segments. 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 individual instruction segments. Our results in a game environment and a simulated robotic gripper setting underscore the importance of segmentation; randomly sampled segments diminish performance, while incorporating labelled segments from PS improves policy performance to the level of a policy trained on twice the amount of labelled data
Stability and Generalization of Zeroth-Order Decentralized Stochastic Gradient Descent with Changing Topology
No full textZeroth-order (ZO) optimization as the gradient-free method has become a powerful tool when the first-order gradient is unavailable or expensive to obtain, especially in decentralized learning scenarios where data and computational resources are distributed across multiple clients. There have been many efforts to analyze the optimization convergence rate of zeroth-order decentralized stochastic gradient descent (ZO-DSGD) algorithms. However, the generalization of these methods has not been well studied. In this paper, we provide a generalization analysis of ZO-DSGD with changing topology, where the clients run zeroth-order SGD with local data and communicate with each other according to time-varying topology. We systematically analyze the generalization error in convex, strongly convex, and non-convex cases. The obtained results in the convex and strongly convex cases with zeroth-order oracles recover the results of SGD. Moreover, the generalization bounds derived in non-convex cases align with that of DSGD. To capture the influence of communication topology on the generalization performance, we analyze local generalization bounds concerning local models held at different clients. The obtained results reflect the influence of the number of clients, local sample size, and topology on the generalization error. To the best of our knowledge, this is the first work that provides a generalization analysis of zeroth-order decentralized stochastic gradient descent methods and recovers the results of SGD
Adaptive Prompt-Based Semantic Embedding with Inspire Potential of Implicit Knowledge for Cross-Modal Retrieval
No full textIn the era of big data, cross-modal retrieval is increasingly important in research and application. Given the latent complexity and non-intuitive nature of cross-modal relationships, leveraging external knowledge such as large models has become a popular approach to facilitate modality alignment. Existing methods typically address these challenges by fine-tuning model encoders or using a fixed number of prompts. However, these approaches struggle with the significant information asymmetry between image-text pairs and the high distribution diversity of image data. These limitations not only introduce noise during training but also reduce the accuracy and generalization capabilities in cross-modal retrieval tasks. To address the above issues, this paper proposes Adaptive Prompt-Based Semantic Embedding with Inspired Potential of Implicit Knowledge (APSE-IPIK). On one hand, we propose an inspired potential strategy to extract fine-grained and multi-perspective text descriptions from large-scale pre-trained multimodal models, which can be seen as implicit knowledge injection. These descriptions are integrated into the visual-semantic embedding through cross-modal semantic alignment with images, balancing the information asymmetry between modalities and reducing the embedding of inaccurate mapping relationships. On the other hand, we construct an instance-level query-based prompt pool strategy to adaptively extract the most relevant prompts, addressing alignment biases caused by intra-modal (especially image) data diversity and improving alignment accuracy. Extensive experiments are conducted on two widely used datasets, Flickr30k and MSCOCO, which show the effectiveness of the proposed method
M3Net: Efficient Time-Frequency Integration Network with Mirror Attention for Audio Classification on Edge
No full textAudio classification plays a crucial role within fields such as human-machine interaction and intelligent robotics. However, high-performance audio classification systems typically demand significant computational and storage resources, posing substantial challenges when deploying to the resource-constrained edge devices with an urgent need for such capabilities. To achieve a new level of balance between model complexity and performance, we introduce a novel multi-view method for the separated time-frequency features extraction and utilization, which exists within the proposed Mini Mirror Multi-View Network (M3Net) in the form of the Mirror Attention mechanism. M3Net enables reversible spatial transformation of spectral features is capable of efficiently leverages robust local and global features in the time and frequency domains with low requirements for parameters. Experiments based on Mel-Spectrogram without data augmentation and pre-training indicate that M3Net can achieve classification accuracy over 97% on the UrbanSound8K and SpeechCommandsV2 datasets with only 0.03 million parameters. The contribution of each functional segment in M3Net is fully verified and explained in the ablation experiments
Dimension Reduction for Symbolic Regression
No full textSolutions of symbolic regression problems are expressions that are composed of input variables and operators from a finite set of function symbols. One measure for evaluating symbolic regression algorithms is their ability to recover formulae, up to symbolic equivalence, from finite samples. Not unexpectedly, the recovery problem becomes harder when the formula gets more complex, that is, when the number of variables and operators gets larger. Variables in naturally occurring symbolic formulas often appear only in fixed combinations. This can be exploited in symbolic regression by substituting one new variable for the combination, effectively reducing the number of variables. However, finding valid substitutions is challenging. Here, we address this challenge by searching over the expression space of small substitutions and testing for validity. The validity test is reduced to a test of functional dependence. The resulting iterative dimension reduction procedure can be used with any symbolic regression approach. We show that it reliably identifies valid substitutions and significantly boosts the performance of different types of state-of-the-art symbolic regression algorithms
Discovering Symmetries of ODEs by Symbolic Regression
No full textSolving systems of ordinary differential equations (ODEs) is essential when it comes to understanding the behavior of dynamical systems. Yet, automated solving remains challenging, in particular for nonlinear systems. Computer algebra systems (CASs) provide support for solving ODEs by first simplifying them, in particular through the use of Lie point symmetries. Finding these symmetries is, however, itself a difficult problem for CASs. Recent works in symbolic regression have shown promising results for recovering symbolic expressions from data. Here, we adapt search-based symbolic regression to the task of finding generators of Lie point symmetries.
With this approach, we can find symmetries of ODEs that existing CASs cannot find
Singular Value Scaling: Efficient Generative Model Compression via Pruned Weights Refinement
No full textWhile pruning methods effectively maintain model performance without extra training costs, they often focus solely on preserving crucial connections, overlooking the impact of pruned weights on subsequent fine-tuning or distillation, leading to inefficiencies. Moreover, most compression techniques for generative models have been developed primarily for GANs, tailored to specific architectures like StyleGAN, and research into compressing Diffusion models has just begun. Even more, these methods are often applicable only to GANs or Diffusion models, highlighting the need for approaches that work across both model types. In this paper, we introduce Singular Value Scaling (SVS), a versatile technique for refining pruned weights, applicable to both model types. Our analysis reveals that pruned weights often exhibit dominant singular vectors, hindering fine-tuning efficiency and leading to suboptimal performance compared to random initialization. Our method enhances weight initialization by minimizing the disparities between singular values of pruned weights, thereby improving the fine-tuning process. This approach not only guides the compressed model toward superior solutions but also significantly speeds up fine-tuning. Extensive experiments on StyleGAN2, StyleGAN3 and DDPM demonstrate that SVS improves compression performance across model types without additional training costs
Accurate Link Prediction for Edge-Incomplete Graphs via PU Learning
No full textGiven an edge-incomplete graph, how can we accurately find its missing links?
The problem aims to discover the missing relations between entities when their relationships are represented as a graph.
Edge-incomplete graphs are prevalent in real-world due to practical limitations, such as not checking all users when adding friends in a social network.
Addressing the problem is crucial for various tasks, including recommending friends in social networks and finding references in citation networks.
However, previous approaches rely heavily on the given edge-incomplete (observed) graph, making it challenging to consider the missing (unobserved) links.
In this paper, we propose PULL, an accurate link prediction method based on the positive-unlabeled (PU) learning.
PULL treats the observed edges in the training graph as positive examples, and the unconnected node pairs as unlabeled ones.
PULL effectively prevents the link predictor from blindly trusting the observed graph by proposing latent variables for every edge, and leveraging the expected graph structure with respect to these variables.
Extensive experiments on real- world datasets show that PULL consistently outperforms the baselines for predicting links in edge-incomplete graphs
Salient Frequency-aware Exemplar Compression for Resource-constrained Online Continual Learning
No full textOnline Class-Incremental Learning (OCIL) enables a model to learn new classes from a data stream. Since data stream samples are seen only once and the capacity of storage is constrained, OCIL is particularly susceptible to Catastrophic Forgetting (CF). While exemplar replay methods alleviate CF by storing representative samples, the limited capacity of the buffer inhibits capturing the entire old data distribution, leading to CF. In this regard, recent papers suggest image compression for better memory usage. However, existing methods raise two concerns: computational overhead and compression defects. On one hand, computational overhead can limit their applicability in OCIL settings, as models might miss learning opportunities from the current streaming data if computational resources are budgeted and preoccupied with compression. On the other hand, typical compression schemes demanding low computational overhead, such as JPEG, introduce noise detrimental to training. To address these issues, we propose Salient Frequency-aware Exemplar Compression (SFEC), an efficient and effective JPEG-based compression framework. SFEC exploits saliency information in the frequency domain to reduce negative impacts from compression artifacts for learning. Moreover, SFEC employs weighted sampling for exemplar elimination based on the distance between raw and compressed data to mitigate artifacts further. Our experiments employing the baseline OCIL method on benchmark datasets such as CIFAR-100 and Mini-ImageNet demonstrate the superiority of SFEC over previous exemplar compression methods in streaming scenarios
A Stochastic Approach to Bi-Level Optimization for Hyperparameter Optimization and Meta Learning
No full textWe tackle the general differentiable meta learning problem that is ubiquitous in modern deep learning, including hyperparameter optimization, loss function learning, few-shot learning and more. These problems are often formalized as Bi-Level Optimizations (BLO). We introduce a novel perspective by turning a given BLO problem into a stochastic optimization, where the inner loss function becomes a smooth probability distribution, and the outer loss becomes an expected loss over the inner distribution. To solve this stochastic optimization, we adopt Stochastic Gradient Langevin Dynamics (SGLD) MCMC to sample inner distribution, and propose a recurrent algorithm to compute the MC-estimated hypergradient. Our derivation is similar to forward-mode differentiation, but we introduce a new first-order approximation that makes it feasible for large models without needing to store huge Jacobian matrices. The main benefits are two fold: i) Our stochastic formulation takes into account uncertainty, which makes the method robust to suboptimal inner optimization or non-unique multiple inner minima due to overparametrization; ii) Compared to existing methods that often exhibit unstable behavior and hyperparameter sensitivity in practice, our method leads to considerably more reliable solutions. We demonstrate that the new approach achieves promising results on diverse meta learning problems and easily scales to learning 87M hyper-parameters in the case of Vision Transformers