United States

Predicting extreme weather events such as tropical and extratropical cyclones
is of vital scientific and societal importance. Of late, machine learning methods have found their way to weather analysis and prediction, but mostly, these methods use machine learning merely as a complement to traditional numerical  weather  prediction models. Although some pure machine learning and data-driven approaches for weather prediction have been developed, they mainly formulate the problem similar to pattern recognition or follow the train of thought of traditional time-series models for extreme weather event forecasting; for the former, this usually yields only single-step ahead prediction, and for the latter, this lacks the flexibility to account for observed weather features as such methods concern only the patterns of the extreme weather occurrences. In this paper, we depart from the typical practice of pattern recognition and time-series approaches and focus on employing machine learning to estimate the probabilities of extreme weather occurrences in a multi-step-ahead (MSA) fashion given information on both weather features and the realized occurrences of extreme weather. Specifically, we propose a Markov conditional forward (MCF) model that adopts the Markov property between the occurrences of extreme weather  for MSA extreme weather forecasting. Moreover, for better long-term prediction, we propose three novel cube perturbation methods to address error accumulation in our model. Experimental results on a real-world extreme weather dataset show the superiority of the proposed MCF model in terms of prediction accuracy for both short-term and long-term forecasting; moreover, the three cube perturbation methods successfully increase the fault tolerance and generalization ability of the MCF model, yielding significant improvements for long-term prediction.

AAAI 2021

A Multi-Step-Ahead Markov Conditional Forward Model with Cube Perturbations for Extreme Weather Forecasting

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

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-26 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.<br><br>

To access this event page, you need to log in with the **email address you registered with**. <br>Access credentials will be sent to your email from Underline -  subject line "Welcome to AAAI 2026". Please be sure to check your spam email folder if you do not see an email confirmation right away.

Please log in

To access this event page, you are required to register.
Please complete your registration to continue.

We recommend reading [**the registration information**](https://aaai.org/conference/aaai/aaai-26/registration/) first.

**Online Registration Form**: https://aaai.getregistered.net/conference-2026 

Registration Required

AAAI 2026

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

technical paper

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. 

It is important for task-oriented dialogue systems to discover the dialogue structure (i.e. the general dialogue flow) from dialogue corpora automatically. Previous work models dialogue structure by extracting latent states for each utterance first and then calculating the transition probabilities among states. These two-stage methods ignore the contextual information when calculating the probabilities, which makes the transitions between the states ambiguous. This paper proposes a conversational graph (CG) to represent deterministic dialogue structure where nodes and edges represent the utterance and context information respectively. An unsupervised Edge-Enhanced Graph Auto-Encoder (EGAE) architecture is designed to model local-contextual and global-structural information for conversational graph learning. Furthermore, a self-supervised objective is introduced with the response selection task to guide the unsupervised learning of the dialogue structure. Experimental results on several public datasets demonstrate that the novel model outperforms several alternatives in aggregating utterances with similar semantics. The effectiveness of the learned dialogue structured is also verified by more than 5\% joint accuracy improvement in the downstream task of low resource dialogue state tracking.

Unsupervised Learning of Deterministic Dialogue Structure with Edge-Enhanced Graph Auto-Encoder

We describe a compilation language of backdoor decomposable monotone circuits (BDMCs) which generalizes several concepts appearing in the literature, e.g. DNNFs and backdoor trees. A C-BDMC sentence is a monotone circuit which satisfies decomposability property (such as in DNNF) in which the inputs (or leaves) are associated with CNF encodings from a given base class C. We consider the class of propagation complete (PC) encodings as a base class and we show that PC-BDMCs are polynomially equivalent to PC encodings. Additionally, we use this to determine the properties of PC-BDMCs and PC encodings with respect to the knowledge compilation map including the list of efficient operations on the languages.

Backdoor Decomposable Monotone Circuits and Propagation Complete Encodings

Deep spiking neural network (DSNN) is a promising computational model towards artificial intelligence. It benefits from both the DNNs and SNNs through a hierarchy structure to extract multiple levels of abstraction and the event-driven computational manner to provide ultra-low-power neuromorphic implementation, respectively. However, how to efficiently train the DSNNs remains an open question because of the non-differentiable spike function that prevents the traditional back-propagation (BP) learning algorithm directly applied to DSNNs. Here, inspired by the findings from the biological neural networks, we address the above-mentioned problem by introducing neural oscillation and spike-phase information to DSNNs. Specifically, we propose an Oscillation Postsynaptic Potential (Os-PSP) and phase-locking active function, and further put forward a new spiking neuron model, namely Resonate Spiking Neuron (RSN). Based on the RSN, we propose a Spike-Level-Dependent Back-Propagation (SLDBP) learning algorithm for DSNNs. Experimental results show that the proposed learning algorithm resolves the problems caused by the incompatibility between the BP learning algorithm and SNNs, and achieves state-of-the-art performance in single spike-based learning algorithms. This work investigates the contribution of introducing biologically inspired mechanisms, such as neural oscillation and spike-phase information to DSNNs and providing a new perspective to design future DSNNs.

Deep Spiking Neural Network with Neural Oscillation and Spike-Phase information

Driven by deep neural networks (DNN), the recent development of computer vision makes vision sensors such as stereo cameras and  Lidars ubiquitous in autonomous cars, robotics and traffic monitoring. However,  a traditional DNN-based data fusion pipeline like object tracking has to hard-wire an engineered set of DNN models to a fixed processing logic, which makes it difficult to  infuse new models to that pipeline. To overcome this, we propose a novel neural-symbolic stream reasoning approach realised by semantic stream reasoning programs which specify DNN-based data fusion pipelines via logic rules with learnable probabilistic degrees as weights. The reasoning task over this program is governed by a novel incremental reasoning algorithm,  which lends itself also as a core building block for a scalable and parallel algorithm to learn the weights for such program. Extensive experiments  with our first prototype  on multi-object tracking benchmarks for autonomous driving and traffic monitoring show that our flexible approach can considerably improve both accuracy and processing throughput compared to the DNN-based counterparts.

A Scalable Reasoning and Learning Approach for Neural-Symbolic Stream Fusion

Solution concepts of traditional game theory assume entirely rational players; therefore, their ability to exploit subrational opponents is limited. One type of subrationality that describes human behavior well is the quantal response. While there exist algorithms for computing solutions against quantal opponents, they either do not scale or may provide strategies that are even worse than the entirely-rational Nash strategies. This paper aims to analyze and propose scalable algorithms for computing effective and robust strategies against a quantal opponent in normal-form and extensive-form games. Our contributions are: 
(1) we define two different solution concepts related to exploiting quantal opponents and analyze their properties; 
(2) we prove that computing these solutions is computationally hard;
(3) therefore, we evaluate several heuristic approximations based on scalable counterfactual regret minimization (CFR);
and (4) we identify a CFR variant that exploits the bounded opponents better than the previously used variants while being less exploitable by the worst-case perfectly-rational opponent.

Complexity and Algorithms for Exploiting Quantal Opponents in Large Two-Player Games

This paper presents a new family of backpropagation-free neural architectures, Gated Linear Networks (GLNs). What distinguishes GLNs from contemporary neural networks is the distributed and local nature of their credit assignment mechanism; each neuron directly predicts the target, forgoing the ability to learn feature representations in favor of rapid online learning. Individual neurons are able to model nonlinear functions via the use of data-dependent gating in conjunction with online convex optimization. We show that this architecture gives rise to universal learning capabilities in the limit, with effective model capacity increasing as a function of network size in a manner comparable with deep ReLU networks. Furthermore, we demonstrate that the GLN learning mechanism possesses extraordinary resilience to catastrophic forgetting, performing almost on par to an MLP with dropout and Elastic Weight Consolidation on standard benchmarks.

Gated Linear Networks

Pre-trained language models have achieved state-of-the-art accuracies on various text classification tasks, e.g., sentiment analysis, natural language inference, and semantic textual similarity. However, the reliability of the fine-tuned text classifiers is an often underlooked performance criterion. For instance, one may desire a model that can detect out-of-distribution (OOD) samples (drawn far from training distribution) or be robust against domain shifts. We claim that one central obstacle to the reliability is the over-reliance of the model on a limited number of keywords, instead of looking at the whole context. In particular, we find that (a) OOD samples often contain in-distribution keywords, while (b) cross-domain samples may not always contain keywords; over-relying on the keywords can be problematic for both cases. In light of this observation, we propose a simple yet effective fine-tuning method, coined masked keyword regularization (MASKER), that facilitates context-based prediction. MASKER regularizes the model to reconstruct the keywords from the rest of the words and make low-confidence predictions without enough context. When applied to various pre-trained language models (e.g., BERT, RoBERTa, and ALBERT), we demonstrate that MASKER improves OOD detection and cross-domain generalization without degrading classification accuracy. Code is available at https://github.com/alinlab/MASKER.

MASKER: Masked Keyword Regularization for Reliable Text Classification

Unsupervised domain adaptation (UDA) assumes that source and target domain data are freely available and usually trained together to reduce the domain gap. However, considering the data privacy and the inefficiency of data transmission, it is impractical in real scenarios. Hence, it draws our eyes to optimize the network in the target domain without accessing labeled source data. To explore this direction in object detection, for the first time, we propose a source data-free domain adaptive object detection (SFOD) framework via modeling it into a problem of learning with noisy labels. Generally, a straightforward method is to leverage the pre-trained network from the source domain to generate the pseudo labels for target domain optimization. However, it is difficult to evaluate the quality of pseudo labels since no labels are available in target domain. In this paper, self-entropy descent (SED) is a metric proposed to search an appropriate confidence threshold for reliable pseudo label generation without using any handcrafted labels. Nonetheless, completely clean labels are still unattainable. After a thorough experimental analysis, false negatives are found to dominate in the generated noisy labels. Undoubtedly, false negatives mining is helpful for performance improvement, and we ease it to false negatives simulation through data augmentation like Mosaic. Extensive experiments conducted in four representative adaptation tasks have demonstrated that the proposed framework can easily achieve state-of-the-art performance. From another view, it also reminds the UDA community that the labeled source data are not fully exploited in the existing methods.

A Free Lunch for Unsupervised Domain Adaptive Object Detection without Source Data

Modeling non-linear data as symmetric positive definite (SPD) matrices on Riemannian manifolds has attracted much attention for various classification tasks. In the context of deep learning, SPD matrix-based Riemannian networks have been shown to be a promising solution for classifying electroencephalogram (EEG) signals, capturing the Riemannian geometry within their structured 2D feature representation. However, existing approaches usually learn spatial-temporal structures in an embedding space for all available EEG signals, and their optimization procedures rely on computationally expensive iterations. Furthermore, these approaches often struggle to encode all of the various types of relationships into a single distance metric, resulting in a loss of generality. To address the above limitations, we propose a Riemannian Embedding Banks method, which divides the problem of common spatial patterns learning in an entire embedding space into K-subproblems and builds one model for each subproblem, to be combined with SPD neural networks. By leveraging the concept of the "separate to learn" technology on a Riemannian manifold, REB divides the data and the embedding space into K non-overlapping subsets and learns K separate distance metrics in a Riemannian geometric space instead of the vector space. Then, the learned K non-overlapping subsets are grouped into neurons in the SPD neural network's embedding layer. Experimental results on public EEG datasets demonstrate the superiority of the proposed approach for learning common spatial patterns of EEG signals despite their non-stationary nature, increasing the convergence speed while maintaining generalization.

Riemannian Embedding Banks for Common Spatial Patterns with EEG-Based SPD Neural Networks

Data augmentation is one of the most effective ways to stabilize learning by improving the generalization of machine-learning models. In recent years, automatic data augmentation methods, such as AutoAugment or Fast AutoAugment have been attracting attention; and these methods improved the results of image classification and object detection tasks. However, several problems remain. Most notably, a larger training dataset requires higher computational costs. When searching with a small dataset in an attempt to determine the data augmentation approach, the true data space and sampling data space do not fully correspond with each other, thereby causing the generalization performance to deteriorate. Moreover, in the existing automatic augmentation methods, the search phase is often dominated by an exceptional sub-policy, which results in a loss of diversity of operations. In this study, we solved these problems by introducing evolutionary computation to previous methods. As mentioned earlier, maintaining diversity is essential. Therefore, we adopted the thermodynamical genetic algorithm (TDGA), which can control the population diversity with a specific genetic operator, known as the thermodynamical selection rule. To confirm the effectiveness of the proposed method, computational experiments were conducted using two benchmark datasets, CIFAR-10 and SVHN, as examples. The experimental results show that the proposed method can obtain various useful augmentation sub-policies for the problems while reducing the computational cost.

Downloads

Next from AAAI 2021

Unsupervised Learning of Deterministic Dialogue Structure with Edge-Enhanced Graph Auto-Encoder

Similar lecture

WeatherEdit: Controllable Weather Editing with 4D Gaussian Field

.css-70qvj9{display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}Downloads

Next from AAAI 2021

Unsupervised Learning of Deterministic Dialogue Structure with Edge-Enhanced Graph Auto-Encoder

Similar lecture

WeatherEdit: Controllable Weather Editing with 4D Gaussian Field

Downloads