Canada

Spiking neural networks (SNNs) exploit neural spikes to provide solutions for low-power intelligent applications on neuromorphic hardware. Although SNNs have high computational efficiency due to spiking communication, they still lack resistance to adversarial attacks and noise perturbations. In the brain, neuronal responses generally possess stochasticity induced by ion channels and synapses, while the role of stochasticity in computing tasks is poorly understood. Inspired by this, we elaborate a stochastic gating spiking neural model for layer-by-layer spike communication, introducing stochasticity to SNNs. Through theoretical analysis, our gating model can be viewed as a regularizer that prevents error amplification under attacks. Meanwhile, our work can explain the robustness of Poisson coding. Experimental results prove that our method can be used alone or with existing robust enhancement algorithms to improve SNN robustness and reduce SNN energy consumption. We hope our work will shed new light on the role of stochasticity in the computation of SNNs. Our code is available at https://github.com/DingJianhao/StoG-meets-SNN/.

AAAI 2024

Enhancing the Robustness of Spiking Neural Networks with Stochastic Gating Mechanisms | VIDEO

stochasticity

spiking neural networks

adversarial robustness

The purpose of the AAAI conference is to promote research in artificial intelligence (AI) and scientific exchange among AI researchers, practitioners, scientists, and engineers in affiliated disciplines. 

AAAI 2021

technical paper

We are pleased to announce the Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI-24), which will be held in Vancouver, British Columbia at the Vancouver Convention Centre – West Building from 20-27 February, 2024.

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-24 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.

We expect for AAAI-24 to be an in-person conference – one author of all accepted papers will be expected to present work in person unless there are exceptional circumstances that prevent this.<br><br><br><br>

In order to access the AAAI-24 event page you need to register [here](https://aaai.org/aaai-conference/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. 

Curriculum adversarial training empirically finds that gradually increasing the hardness of adversarial examples can further improve the adversarial robustness of the trained model compared to conventional adversarial training. However, theoretical understanding of this strategy remains limited. In an attempt to bridge this gap, we analyze the adversarial training process from an online perspective. Specifically, we treat adversarial examples in different iterations as samples from different adversarial distributions. We then introduce the time series prediction framework and deduce novel generalization error bounds. Our theoretical results not only demonstrate the effectiveness of the conventional adversarial training algorithm but also explain why curriculum adversarial training methods can further improve adversarial generalization. We conduct comprehensive experiments to support our theory.

A Closer Look at Curriculum Adversarial Training: From an Online Perspective

Reconstructing hand-held objects from monocular RGB images is an appealing yet challenging task. In this task, contacts between hands and objects provide important cues for recovering the 3D geometry of the hand-held objects. Though recent works have employed implicit functions to achieve impressive progress, they ignore formulating contacts in their frameworks, which results in producing less realistic object meshes. In this work, we explore how to model contacts in an explicit way to benefit the implicit reconstruction of hand-held objects. Our method consists of two components: explicit contact prediction and implicit shape reconstruction. In the first part, we propose a new subtask of directly estimating 3D hand-object contacts from a single image. The part-level and vertex-level graph-based transformers are cascaded and jointly learned in a coarse-to-fine manner for more accurate contact probabilities. In the second part, we introduce a novel method to diffuse estimated contact states from the hand mesh surface to nearby 3D space and leverage diffused contact probabilities to construct the implicit neural representation for the manipulated object. Benefiting from estimating the interaction patterns between the hand and the object, our method can reconstruct more realistic object meshes, especially for object parts that are in contact with hands. Extensive experiments on challenging benchmarks show that the proposed method outperforms the current state of the arts by a great margin. Our code is publicly available at https://junxinghu.github.io/projects/hoi.html.

Learning Explicit Contact for Implicit Reconstruction of Hand-Held Objects from Monocular Images

Object rearrangement in a multi-room setup should produce a reasonable plan that reduces the agent's overall travel and the number of steps. Recent state-of-the-art methods fail to produce such plans because they rely on explicit exploration for discovering unseen objects due to partial observability and a heuristic planner to sequence the actions for rearrangement. This paper proposes a novel task planner to efficiently plan a sequence of actions to discover unseen objects and rearrange misplaced objects within an untidy house to achieve a desired tidy state. The proposed method introduces several innovative techniques, including (i) a method for discovering unseen objects using commonsense knowledge from large language models, (ii) a collision resolution and buffer prediction method based on Cross-Entropy Method to handle blocked goal and swap cases, (iii) a directed spatial graph-based state space for scalability, and (iv) deep reinforcement learning (RL) for producing an efficient plan to simultaneously discover unseen objects and rearrange the visible misplaced ones to minimize the overall traversal. The paper also presents new metrics and a benchmark dataset called MoPOR to evaluate the effectiveness of the rearrangement planning in a multi-room setting. The experimental results demonstrate that the proposed method effectively addresses the multi-room rearrangement problem.

Task Planning for Object Rearrangement in Multi-Room Environments

Multimodal Sentiment Analysis (MSA) has attracted widespread research attention recently. Most MSA studies are based on the assumption of modality completeness. However, many inevitable factors in real-world scenarios lead to uncertain missing modalities, which invalidate the fixed multimodal fusion approaches. To this end, we propose a Unified multimodal Missing modality self-Distillation Framework (UMDF) to handle the problem of uncertain missing modalities in MSA. Specifically, a unified self-distillation mechanism in UMDF drives a single network to automatically learn robust inherent representations from the consistent distribution of multimodal data. Moreover, we present a multi-grained crossmodal interaction module to deeply mine the complementary semantics among modalities through coarse- and fine-grained crossmodal attention. Eventually, a dynamic feature integration module is introduced to enhance the beneficial semantics in incomplete modalities while filtering the redundant information therein to obtain a refined and robust multimodal representation. Comprehensive experiments on three datasets demonstrate that our framework significantly improves MSA performance under both uncertain missing-modality and complete-modality testing conditions.

A Unified Self-Distillation Framework for Multimodal Sentiment Analysis with Uncertain Missing Modalities

Consumer-grade cameras capture the RAW physical description of a scene and then process the image signals to obtain high-quality RGB images that are faithful to human visual perception. Conventionally, dense prediction scenes require high-precision recognition of objects in RGB images. However, predicting RGB data to exhibit the expected adaptability and robustness in harsh environments can be challenging. By capitalizing on the broader color gamut and higher bit depth offered by RAW data, in this paper, we demonstrate that RAW data can significantly improve the accuracy and robustness of object detectors in harsh environments. Firstly, we propose a general Pipeline for RAW Detection (PRD), along with a preprocessing strategy tailored to RAW data. Secondly, we design the RAW Corruption Benchmark (RCB) to address the dearth of benchmarks that reflect realistic scenarios in harsh environments. Thirdly, we demonstrate the significant improvement of RAW images in object detection for low-light and corrupt scenes. Specifically, our experiments indicate that PRD (using FCOS) outperforms RGB detection by 13.9mAP on LOD-Snow without generating restored images. Finally, we introduce a new nonlinear method called Functional Regularization (FR), which can effectively mine the unique characteristics of RAW data. The code is available at https://github.com/DreamerCCC/RawMining.

Guiding a Harsh-Environments Robust Detector via RAW Data Characteristic Mining

Natural language processing models are vulnerable to adversarial examples. Previous textual adversarial attacks adopt model internal information (gradients or  confidence scores) to generate adversarial examples. However, this information is unavailable in the real world. Therefore, we focus on a more realistic and challenging setting, named hard-label attack, in which the attacker can only query the model and obtain a discrete prediction label. Existing hard-label attack algorithms tend to initialize adversarial examples by random substitution and then utilize complex heuristic algorithms to optimize the adversarial perturbation. These methods require a lot of  model queries and the attack success rate is restricted by adversary initialization. In this paper,  we propose a novel hard-label attack algorithm named LimeAttack, which leverages a local explainable method to approximate word importance ranking, and then adopts beam search to find the optimal solution. Extensive experiments show that LimeAttack achieves the better attacking performance compared with existing hard-label attack under the same query budget. In addition, we evaluate the effectiveness of LimeAttack on large language models and some defense methods, and results indicate that adversarial examples remain a significant threat to  large language models. The adversarial examples crafted by LimeAttack  are highly transferable and  effectively improve model robustness in adversarial training.

LimeAttack: Local Explainable Method for Textual Hard-Label Adversarial Attack

We study the problem of global optimization, where we analyze the performance of the Piyavskii--Shubert algorithm and its variants. For any given time duration $T$, instead of the extensively studied simple regret (which is the difference of the losses between the best estimate up to $T$ and the global minimum), we study the cumulative regret up to time $T$. For $L$-Lipschitz continuous functions, we show that the cumulative regret is $O(L\log T)$. For $H$-Lipschitz smooth functions, we show that the cumulative regret is $O(H)$. We analytically extend our results for functions with Hölder continuous derivatives, which cover both the Lipschitz continuous and the Lipschitz smooth functions, individually. We further show that a simpler variant of the Piyavskii-Shubert algorithm performs just as well as the traditional variants for the Lipschitz continuous or the Lipschitz smooth functions. We further extend our results to broader classes of functions, and show that, our algorithm efficiently determines its queries; and achieves nearly minimax optimal (up to log factors) cumulative regret, for general convex or even concave regularity conditions on the extrema of the objective (which encompasses many preceding regularities). We consider further extensions by investigating the performance of the Piyavskii-Shubert variants in the scenarios with unknown regularity, noisy evaluation and multivariate domain.

Cumulative Regret Analysis of the Piyavskii–Shubert Algorithm and Its Variants for Global Optimization

Two-sided matching markets have been widely studied in the literature due to their rich applications. Since participants are usually uncertain about their preferences, online algorithms have recently been adopted to learn them through iterative interactions. \citet{wang2022bandit} initiate the study of this problem in a many-to-one setting with \textit{responsiveness}. However, their results are far from optimal and lack guarantees of incentive compatibility. An extension of \citet{kong2023player} to this more general setting achieves a near-optimal bound for player-optimal regret. Nevertheless, due to the substantial requirement for collaboration, a single player's deviation could lead to a huge increase in its own cumulative rewards and an $O(T)$ regret for others. In this paper, we aim to enhance the regret bound in many-to-one markets while ensuring incentive compatibility. We first propose the adaptively explore-then-deferred-acceptance (AETDA) algorithm for responsiveness setting and derive an $O(N\min\set{N,K}C\log T/\Delta^2)$ upper bound for player-optimal stable regret while demonstrating its guarantee of incentive compatibility, where $N$ represents the number of players, $K$ is the number of arms, $T$ denotes the time horizon, $C$ is arms' total capacities and $\Delta$ signifies the minimum preference gap among players. This result is a significant improvement over \citet{wang2022bandit}. And to the best of our knowledge, it constitutes the first player-optimal guarantee in matching markets that offers such robust assurances. We also consider broader \textit{substitutable} preferences, one of the most general conditions to ensure the existence of a stable matching and cover responsiveness. We devise an online DA (ODA) algorithm and establish an $O(NK\log T/\Delta^2)$ player-pessimal stable regret bound for this setting. Compared with \citet{wang2022bandit}, this algorithm not only achieves a better result but also applies to more general markets.

Improved Bandits in Many-to-One Matching Markets with Incentive Compatibility

Randomized smoothing (RS) provides state-of-the-art (SOTA) certified robustness against $L_2$-perturbations for large neural networks. Among studies in this field, methods based on adversarial training (AT) achieve remarkably robust performance by applying adversarial examples to construct the smoothed classifier. These AT-based RS methods typically seek a pointwise adversary that generates the worst-case adversarial examples by perturbing each input independently. However, there are unexplored benefits to considering such adversarial robustness across the entire data distribution. To this end, we provide a novel framework called DRF, which connects AT-based RS methods with distributional robustness (DR), and show that these methods are special cases of their counterparts in our framework. Due to the advantages conferred by DR, our framework can control the trade-off between the clean accuracy and certified robustness of smoothed classifiers to a significant extent. Our experiments demonstrate that DRF can substantially improve the certified $L_2$-robustness of AT-based RS.

DRF: Improving Certified Robustness via Distributional Robustness Framework

Low-level vision plays a crucial role in a wide range of imaging quality and image recognition applications. However, the limited size, quality, and diversity of datasets often pose significant challenges for low-level tasks. Data augmentation is the most effective and practical way of sample expansion, but the commonly used augmentation methods in high-level tasks have limited improvement in the low-level due to the boundary effects or the non-realistic context information. In this paper, we propose the Cut-and-Swap Frequency Components (CutFreq) method for low-level vision, which aims to preserve high-level representations with directionality and improve image synthesis quality. Observing the significant frequency domain differences between reconstructed images and real ones, in CutFreq, we propose to transform the input and real images separately in the frequency domain, then define two stages for the model training process, and finally swap the specified frequency bands respectively and inversely transform to generate augmented samples. The experimental results show the superior performance of CutFreq on five low-level vision tasks. Moreover, we demonstrate the effectiveness of CutFreq in the low-data regime. Code is available at https://github.com/DreamerCCC/CutFreq.

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