Canada

Multi-source domain adaptation (MSDA) aims to transfer knowledge from multiple source domains to the unlabeled target domain. In this paper, we propose a cycle self-refinement domain adaptation method, which progressively attempts to learn the dominant transferable knowledge in each source domain in a cycle manner. Specifically, several source-specific networks and a domain-ensemble network are adopted in the proposed method. The source-specific networks are adopted to provide the dominant transferable knowledge in each source domain for instance-level ensemble on predictions of the samples in target domain. Then these samples with high-confidence ensemble predictions are adopted to refine the domain-ensemble network. Meanwhile, to guide each source-specific network to learn more dominant transferable knowledge, we force the features of the target domain from the domain-ensemble network and the features of each source domain from the corresponding source-specific network to be aligned with their predictions from the corresponding networks. Thus the adaptation ability of source-specific networks and the domain-ensemble network can be improved progressively. Extensive experiments on Office-31, Office-Home and DomainNet show that the proposed method outperforms the state-of-the-art methods for most tasks.

AAAI 2024

Cycle Self-Refinement for Multi-Source Domain Adaptation

learning & optimization for cv

ensemble methods

domain adaptation

## Welcome to 2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics!
This year, the conference is in Mexico City. NAACL was actually already planned for Mexico City in 2021, but due to the pandemic the entire conference was moved online. This year, finally, we get to go! So it is my sincere pleasure to welcome you to Mexico City, whether in person or virtually. Having the conference in Mexico City is a good opportunity to emphasize that NAACL is our flagship conference for ACL members not only in North America but also in Central and South America, even though NAACL has been bearing “North” in its name. At this year’s conference, we have a theme to match, with a theme track on the Languages of Latin America to showcase the linguistic diversity of the region.
 
The opportunity to present at NAACL should not depend on a researcher’s travel budget, or their family status. This is why it is so important to make virtual participation at NAACL as good an experience as possible – but we want to also provide a good experience for in-person participants. As a community, we are still working out the best way to do that. This year at NAACL, we are trying out a big virtual poster session ahead of the conference, with the hope that this will make make for a lively and interactive experience. At the same time, we are reducing virtual oral presentations, which seem to be particularly tricky to make to work well. A big thanks to the NAACL program chairs and to Luciana Benotti for all their ideas and work to improve the virtual experience. And participants, virtual as well as in-person: Please let us know what worked for you and what didn’t, so we can continue to improve hybrid conferences. 
 
I have been lucky to work with many amazing people. Without their insight, dedication and patience, and without the many hours of work they put in, NAACL would not have been possible. A huge thank you to the program chairs Helena Gomez, Kevin Duh, and Steve Bethard – you are the best! 
 
Finally, I would like to thank all authors, invited speakers and panelists, area chairs and reviewers, the volunteers organizing and chairing sessions, and all attendees, in-person and virtual. Thank you for helping us make NAACL 2024 come to life. 
 
Welcome and hope you all enjoy the conference! 
Katrin Erk 
The University of Texas at Austin 
NAACL-2024 General Chair 
*You can read the full Welcome message in the [Conference Handbook (downloadable)](https://drive.google.com/file/d/1H1NvW0VASQjkSCYgw3mr-3l4yYDSxz6B/view?usp=sharing)*

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NAACL 2024

2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics

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.

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. 

There are two primary approaches to addressing cross-lingual transfer: multilingual pre-training, which implicitly aligns the hidden representations of various languages, and translate-test, which explicitly translates different languages into an intermediate language, such as English. Translate-test offers better interpretability compared to multilingual pre-training. However, it has lower performance than multilingual pre-training and struggles with word-level tasks due to translation altering word order. As a result, we propose a new Machine-created Universal Language (MUL) as an alternative intermediate language. MUL comprises a set of discrete symbols forming a universal vocabulary and a natural language to MUL translator for converting multiple natural languages to MUL. MUL unifies shared concepts from various languages into a single universal word, enhancing cross-language transfer. Additionally, MUL retains language-specific words and word order, allowing the model to be easily applied to word-level tasks. Our experiments demonstrate that translating into MUL yields improved performance compared to multilingual pre-training, and our analysis indicates that MUL possesses strong interpretability. The code is at: https://github.com/microsoft/Unicoder/tree/master/MCUL.

Machine-Created Universal Language for Cross-Lingual Transfer

In this paper, we introduce the problem of rewriting finite formal languages using syntactic macros such that the rewriting is minimal in size. We present polynomial-time algorithms to solve variants of this problem and show their correctness. To demonstrate the practical relevance of the proposed problems and the feasibility and effectiveness of our algorithms in practice, we apply these to biomedical ontologies authored in OWL. We find that such rewritings can significantly reduce the size of ontologies by capturing repeated expressions with macros. This approach not only offers valuable assistance in enhancing ontology quality and comprehension but can also be seen as a general methodology for evaluating features of rewriting systems (including syntactic macros, templates, or other forms of rewriting rules), which can be analyzed in terms of their influence on computational problems.

Minimal Macro-Based Rewritings of Formal Languages: Theory and Applications in Ontology Engineering (and Beyond)

Recently, patch-wise contrastive learning is drawing attention for the image translation by exploring the semantic correspondence between the input image and the output image. To further explore the patch-wise topology for high-level semantic understanding, here we exploit the graph neural network to capture the topology-aware features. Specifically, we construct the graph based on the patch-wise similarity from a pretrained encoder, whose adjacency matrix is shared to enhance the consistency of patch-wise relation between the input and the output. Then, we obtain the node feature from the graph neural network, and enhance the correspondence between the nodes by increasing mutual information using the contrastive loss. In order to capture the hierarchical semantic structure, we further propose the graph pooling. Experimental results demonstrate the state-of-art results for the image translation thanks to the semantic encoding by the constructed graphs.

Patch-Wise Graph Contrastive Learning for Image Translation | VIDEO

We present a framework for robots to learn novel visual concepts and visual tasks via in-situ linguistic interactions with human users. Previous approaches in computer vision have either used large pre-trained visual models to infer novel objects zero-shot, or added novel concepts along with their attributes and representations to a concept hierarchy. We extend the approaches that focus on learning visual concept hierarchies and take this ability one step further to demonstrate novel task solving on robots along with the learned visual concepts. To enable a visual concept learner to solve robotics tasks one-shot, we developed two distinct techniques. Firstly, we create a novel approach, Hi-Viscont(HIerarchical VISual CONcept learner for Task), which augments information of a novel concept, that is being taught, to its parent nodes within a concept hierarchy. This information propagation allows all concepts in a hierarchy to update as novel concepts are taught in a continual learning setting. Secondly, we represent a visual task as a scene graph with language annotations, allowing us to create novel permutations of a demonstrated task zero-shot in-situ. We present two sets of results. First, we compared Hi-Viscont with FALCON (Mei et al. 2022) on visual question answering(VQA) in three domains. While being comparable to FALCON on leaf level concepts, Hi-Viscont achieves an improvement of 9% on non-leaf concepts on average. Secondly, we conduct a human subject experiment where users teach our robot visual tasks in-situ, and we compare our model’s performance against FALCON while still using our contextualized scene graph. Our framework achieves 33% improvements in success rate metric, and 19% improvements in the object level accuracy than FALCON. With both these results we demonstrate the ability of our model to learn tasks and concepts in a continual learning setting on the robot.

Interactive Visual Task Learning for Robots

Knowledge-based visual reasoning remains a daunting task since it not only requires machines to interpret the concepts and relationships from visual scenes but also associate them with external world knowledge to conduct a chain of reasoning on open-world questions. Previous works, however, treat visual perception and language-based reasoning as two independent modules, failing to attend to both modules throughout all stages of reasoning. To this end, we propose Visual Chain-of-thought Prompting (VCTP) for knowledge-based reasoning, which involves the interaction between visual content and natural language in an iterative step-by-step reasoning manner. VCTP contains three stages, see, think, and confirm. The see stage scans the image and grounds the visual concept candidates with a visual perception model. The think stage adopts a pre-trained large language model (LLM) to attend to key visual concepts from natural language questions adaptively. It then transforms key visual context into text context for prompting with a visual captioning model, and adopts the LLM to generate the answer. The confirm stage further uses the LLM to generate the supporting rationale to the answer, which is then passed through a cross-modality classifier to verify that it’s consistent with the visual context. We iterate through the think-confirm stages to ensure the verified rationale is consistent with the answer. We conduct experiments on a range of knowledge-based visual reasoning datasets. We found our VCTP enjoys several benefits, 1). it achieves better performance than the previous few-shot learning baselines; 2). it enjoys the total transparency and trustworthiness of the whole reasoning process by providing rationales for each reasoning step; 3). it is computation-efficient compared with other fine-tuning baselines.

Visual Chain-of-Thought Prompting for Knowledge-Based Visual Reasoning

Communication plays a crucial role in information sharing within the field of multi-agent reinforcement learning (MARL). However, how to transmit information that meets individual needs remains a long-standing challenge. Some existing work focus on using a common channel for information transfer, which limits the capability for local communication. Meanwhile, other work attempt to establish peer-to-peer communication topologies but suffer from quadratic complexity. In this paper, we propose Personalized Multi-Agent Communication (PMAC), which enables the formation of peer-to-peer communication topologies, personalized message sending, and personalized message receiving. All these modules in PMAC are performed using only multilayer perceptrons (MLPs) with linear computational complexity. Empirically, we show the strength of personalized communication in a variety of cooperative scenarios. Our approach exhibits competitive performance compared to existing methods while maintaining notable computational efficiency.

PMAC: Personalized Multi-Agent Communication

Multi-view action clustering leverages the complementary information from different camera views to enhance the clustering performance. Although existing approaches have achieved significant progress, they assume all camera views are available in advance, which is impractical when the camera view is incremental over time. Besides, learning the invariant information among multiple camera views is still a challenging issue, especially in continual learning scenario. Aiming at these problems, we propose a novel  continual action clustering (CAC) method, which is capable of learning action categories in a continual learning manner. To be specific, we first devise a category memory library, which captures and stores the learned categories from historical views. Then, as a new camera view arrives, we only need to maintain a consensus partition matrix, which can be updated by leveraging the incoming new camera view rather than keeping all of them. Finally, a three-step alternate optimization is proposed, in which the category memory library and consensus partition matrix are  optimized. The empirical experimental results on 6 realistic multi-view action collections demonstrate the excellent clustering performance and time/space efficiency of the CAC compared with 15 state-of-the-art baselines.

Live and Learn: Continual Action Clustering with Incremental Views

Reinforcement Learning from Demonstrations (RLfD) has emerged as an effective method by fusing expert demonstrations into Reinforcement Learning (RL) training, harnessing the strengths of both Imitation Learning (IL) and RL. However, existing algorithms rely on offline demonstrations, which can introduce a distribution gap between the demonstrations and the actual training environment, limiting their performance. In this paper, we propose a novel approach, Reinforcement Learning from Online Demonstrations (RLfOLD), that leverages online demonstrations to address this limitation, ensuring the agent learns from relevant and up-to-date scenarios, thus effectively bridging the distribution gap. Unlike conventional policy networks used in typical actor-critic algorithms, RLfOLD introduces a policy network that outputs two standard deviations: one for exploration and the other for IL training. This novel design allows the agent to adapt to varying levels of uncertainty inherent in both RL and IL. Furthermore, we introduce an exploration process guided by an online expert, incorporating an uncertainty-based technique. Our experiments on the CARLA NoCrash benchmark demonstrate the effectiveness and efficiency of RLfOLD. Notably, even with a significantly smaller encoder and a single camera setup, RLfOLD surpasses state-of-the-art methods in this evaluation. These results, achieved with limited resources, highlight RLfOLD as a highly promising solution for real-world applications.

RLfOLD: Reinforcement Learning from Online Demonstrations in Urban Autonomous Driving

Constrained Reinforcement Learning has been employed to compute safe policies through the use of trajectory based cost constraints (on expected cost or Value at Risk, VaR cost or Conditional VaR cost). The key challenge is in handling constraints on cost accumulated across time steps. Existing methods have developed innovative ways of converting such trajectory based cost constraint to surrogate RL problems that can be solved easily. While such approaches have provided good solutions with regards to objective, they rely on cost estimates and can therefore be either aggressive or conservative with respect to costs. This is owing to use of estimates for "future" or "backward" costs in local cost constraints.

To that end, we provide an equivalent unconstrained formulation to constrained RL that employs reward penalties over augmented states to compute safe policies. Due to being able to identify specific trajectories that violate cost constraints accurately (and not just in expectation), we are able to provide approaches which have strong empirical performance and theoretical properties. More importantly, this provides a new paradigm for solving richly constrained (e.g., constraints on expected cost, Value at Risk, Conditional Value at Risk) Reinforcement Learning problems effectively. As we show in our experimental results, we are able to outperform leading approaches for different constraint types on multiple benchmark problems.

Reward Penalties on Augmented States for Solving Richly Constrained RL Effectively

Diffusion-based generative models represent the current state-of-the-art for image generation. However, standard diffusion models are based on Euclidean geometry and do not translate directly to manifold-valued data. In this work, we develop extensions of both score-based generative models (SGMs) and Denoising Diffusion Probabilistic Models (DDPMs) to the Lie group of 3D rotations, SO(3). SO(3) is of particular interest in many disciplines such as robotics, biochemistry and astronomy/cosmology science. Contrary to more general Riemannian manifolds, SO(3) admits a tractable solution to heat diffusion, and allows us to implement efficient training of diffusion models. We apply both SO(3) DDPMs and SGMs to synthetic densities on SO(3) and demonstrate state-of-the-art results. Additionally, we demonstrate the practicality of our model on pose estimation tasks and in predicting correlated galaxy orientations for astrophysics/cosmology.

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Machine-Created Universal Language for Cross-Lingual Transfer

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