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Aspect prediction (AP) and sentiment prediction (SP) are representative applications in fine-grained sentiment anal- ysis. They can be considered as sequential tasks, where AP identifies mentioned aspects in a sentence, and SP infers fine-grained sentiments for these aspects. Recent models perform the aspect-sentiment prediction in a joint man-ner, but heavily rely on the feature interactions of aspect and sentiment. One drawback is that they ignore correlation strength varies between aspect features and sentiment fea- tures across different sentences, and employ a fixed feature interaction strategy may limit effective knowledge transfer across tasks. To tackle this issue, in this paper, we propose an Adaptive Inter-task Feature Interaction framework, AIFI, for joint aspect-sentiment prediction. Specifically, we introduce a novel contrast-based alignment method based on contrastive learning. Our approach considers the AP-specific and SP-specific representations of a given sentence as a positive pair, while representation of another random sentence serves as a negative example. Moreover, we propose an inter-task feature correlation network to predict the contrast strength, which is determined by the temperature coefficient in the InfoNCE loss. This dynamic correlation adjustment enhances model’s ability to capture proper feature interactions more efficiently. Experimental results on three datasets validate the effectiveness of our approach.

AAAI 2024

Modeling Adaptive Inter-Task Feature Interactions via Sentiment-Aware Contrastive Learning for Joint Aspect-Sentiment Prediction

sentiment prediction

aspect prediction

contrastive learning

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. 

In this paper, we present UniADS, the first Universal Architecture-Distiller Search framework for co-optimizing student architecture and distillation policies. Teacher-student distillation gap limits the distillation gains. Previous approaches seek to discover the ideal student architecture while ignoring distillation settings. In UniADS, we construct a comprehensive search space encompassing an architectural search for student models, knowledge transformations in distillation strategies, distance functions, loss weights, and other vital settings. To efficiently explore the search space, we utilize the NSGA-II genetic algorithm for better crossover and mutation configurations and employ the Successive Halving algorithm for search space pruning, resulting in improved search efficiency and promising results. Extensive experiments are performed on different teacher-student pairs using CIFAR-100 and ImageNet datasets. The experimental results consistently demonstrate the superiority of our method over existing approaches. Furthermore, we provide a detailed analysis of the search results, examining the impact of each variable and extracting valuable insights and practical guidance for distillation design and implementation.

UniADS: Universal Architecture-Distiller Search for Distillation Gap

Domain learning is the task of finding an action model that can explain given observed plan executions, so-called traces.                                                                                   
It allows us to automate the identification of actions' preconditions and effects instead of relying on hand-modeled expert knowledge.                                                                      
While previous research has put forth various techniques and covers multiple planning formalisms, the theoretical foundations of domain learning are still in their infancy.
                                                                                                                                                                                                          
We investigate the most basic setting, that is grounded classical planning without negative preconditions or conditional effects with full observability of the state variables.                            
The given traces are assumed to be justified in the sense that either no single action or no set of actions can be removed without violating correctness of the plan.                                       
Furthermore, we might be given additional constraints in the form of a propositional logical formula.                                                                                                       
We show the consequences of these assumptions for the computational complexity of identifying a satisfactory planning domain.

Learning Planning Domains from Non-redundant Fully-Observed Traces: Theoretical Foundations and Complexity Analysis

Reconstructing 3D objects from extremely sparse views is a long-standing and challenging problem. While recent techniques employ image diffusion models for generating plausible images at novel viewpoints or for distilling pre-trained diffusion priors into 3D representations using score distillation sampling (SDS), these methods often struggle to simultaneously achieve high-quality, consistent, and detailed results for both novel-view synthesis (NVS) and geometry. In this work, we present Sparse3D, a novel 3D reconstruction method tailored for sparse view inputs. Our approach distills robust priors from a multiview-consistent diffusion model to refine a neural radiance field. Specifically, we employ a controller that harnesses epipolar features from input views, guiding a pre-trained diffusion model, such as Stable Diffusion, to produce novel-view images that maintain 3D consistency with the input. By tapping into 2D priors from powerful image diffusion models, our integrated model consistently delivers high-quality results, even when faced with open-world objects. To address the blurriness introduced by conventional SDS, we introduce the category-score distillation sampling (C-SDS) to enhance detail. We conduct experiments on CO3DV2 which is a multi-view dataset of real-world objects. Both quantitative and qualitative evaluations demonstrate that our approach outperforms previous state-of-the-art works on the metrics regarding NVS and geometry reconstruction.

Sparse3D: Distilling Multiview-Consistent Diffusion for Object Reconstruction from Sparse Views

In this work, we present a novel approach to multi-view action recognition where we guide learned action representations to be separated from view-relevant information in a video. When trying to classify action instances captured from multiple viewpoints, there is a higher degree of difficulty due to the difference in background, occlusion, and visibility of the captured action from different camera angles. To tackle the various problems introduced in multi-view action recognition, we propose a novel configuration of learnable transformer decoder queries, in conjunction with two supervised contrastive losses, to enforce the learning of action features that are robust to shifts in viewpoints. Our disentangled feature learning occurs in two stages: the transformer decoder uses separate queries to separately learn action and view information, which are then further disentangled using our two contrastive losses. We show that our model and method of training significantly outperforms all other uni-modal models on four multi-view action recognition datasets: NTU RGB+D, NTU RGB+D 120, PKU-MMD, and N-UCLA. Compared to previous RGB works, we see maximal improvements of 1.5%, 4.8%, 2.2%, and 4.8% on each dataset, respectively.

DVANet: Disentangling View and Action Features for Multi-View Action Recognition

Designing suitable reward functions for numerous interacting intelligent agents is challenging in real-world applications. Inverse reinforcement learning (IRL) in mean field games (MFGs) offers a practical framework to infer reward functions from expert demonstrations of large-scale multi-agent systems. While promising, the assumption of agent homogeneity in MFGs limits the capability of existing methods to handle demonstrations with heterogeneous objectives, which are common in practice. To this end, we propose a deep latent variable MFG model and an associated IRL method capable of inferring rewards from demonstrations of different yet structurally similar tasks. Our experiments, conducted on simulated scenarios and a real-world spatial taxi-ride pricing problem, demonstrate the superiority of our approach over state-of-the-art IRL methods in MFGs.

Meta-Inverse Reinforcement Learning for Mean Field Games via Probabilistic Context Variables

Proximal Policy Optimization algorithm employing a clipped surrogate objective (PPO-Clip) is a prominent exemplar of the policy optimization methods. However, despite its remarkable empirical success, PPO-Clip lacks theoretical substantiation to date. In this paper, we contribute to the field by establishing the first global convergence results of a PPO-Clip variant in both tabular and neural function approximation settings. Our findings highlight the $O(1/\sqrt{T})$ min-iterate convergence rate specifically in the context of neural function approximation. We tackle the inherent challenges in analyzing PPO-Clip through three central concepts: (i) We introduce a generalized version of the PPO-Clip objective, illuminated by its connection with the hinge loss. (ii) Employing entropic mirror descent, we establish asymptotic convergence for tabular PPO-Clip with direct policy parameterization. (iii) Inspired by the tabular analysis, we streamline convergence analysis by introducing a two-step policy improvement approach. This decouples policy search from complex neural policy parameterization using a regression-based update scheme. Furthermore, we gain deeper insights into the efficacy of PPO-Clip by interpreting these generalized objectives. Our theoretical findings also mark the first characterization of the influence of the clipping mechanism on PPO-Clip convergence. Importantly, the clipping range affects only the pre-constant of the convergence rate.

PPO-Clip Attains Global Optimality: Towards Deeper Understandings of Clipping

We give a quantitative analysis of the independence of irrelevant alternatives (IIA) axiom. IIA says that the society's preference between x and y should depend only on individual preferences between x and y: we show that, in several contexts, if the individuals express their preferences about additional (``irrelevant'') alternatives, this information helps to estimate better which of x and y has higher social welfare.
Our contribution is threefold: (1) we provide a new tool to measure the impact of IIA on social welfare (pairwise distortion), based on the well-established notion of voting distortion, (2) we study the average impact of IIA in both general and metric settings, with experiments on synthetic and real data and (3)  we study the worst-case impact of IIA in the 1D-Euclidean metric space.

Independence of Irrelevant Alternatives under the Lens of Pairwise Distortion | VIDEO

Editing real images authentically while also achieving cross-domain editing remains a challenge. Recent studies have focused on converting real images into latent codes and accomplishing image editing by manipulating these codes. However, merely manipulating the latent codes would constrain the edited images to the generator's image domain, hindering the attainment of diverse editing goals. In response, we propose an innovative image editing method called HyperEditor, which utilizes weight factors generated by hypernetworks to reassign the weights of the pre-trained StyleGAN2's generator. Guided by CLIP's cross-modal image-text semantic alignment, this innovative approach enables us to simultaneously accomplish authentic attribute editing and cross-domain style transfer, a capability not realized in previous methods. Additionally, we ascertain that modifying only the weights of specific layers in the generator can yield an equivalent editing result. Therefore, we introduce an adaptive layer selector, enabling our hypernetworks to autonomously identify the layers requiring output weight factors, which can further improve our hypernetworks' efficiency. Extensive experiments on abundant challenging datasets demonstrate the effectiveness of our method.

HyperEditor: Achieving Both Authenticity and Cross-Domain Capability in Image Editing via Hypernetworks | VIDEO

Symbolic regression, as one of the most crucial tasks in AI for science, discovers governing equations from experimental data. Popular approaches based on genetic programming, Monte Carlo tree search, or deep reinforcement learning learn symbolic regression from a fixed dataset. These methods require massive datasets and long training time especially when learning complex equations involving many variables. Recently, Control Variable Genetic Programming (CVGP) has been introduced which accelerates the regression process by discovering equations from designed control variable experiments. However, the set of experiments is fixed a-priori in CVGP and we observe that sub-optimal selection of experiment schedules delay the discovery process significantly. To overcome this limitation, we propose Racing Control Variable Genetic Programming (Racing-CVGP), which carries out multiple experiment schedules simultaneously. A selection scheme similar to that used in selecting good symbolic equations in the genetic programming process is implemented to ensure that promising experiment schedules eventually win over the average ones. The unfavorable schedules are terminated early to save time for the promising ones. We evaluate Racing-CVGP on several synthetic and real-world datasets corresponding to true physics laws. We demonstrate that Racing-CVGP outperforms CVGP and a series of symbolic regressors which discover equations from fixed datasets.

Racing Control Variable Genetic Programming for Symbolic Regression | VIDEO

Homography estimation is a fundamental problem in computer vision. Previous works mainly focus on estimating either a single homography or multiple homographies based on mesh grid division of the image. In practical scenarios, single homography is inadequate and often leads to a compromised result of multiple planes. On the other hand, mesh grid multi-homography damages the plane distribution of the scene, and does not fully address the problem since different planes may still exist in each single grid.

In this work, we propose a novel semantics guided multi-homography estimation framework, Mask-Homo, to provide an explicit solution to the multi-plane depth disparity problem. First, a pseudo plane mask generation module is designed to obtain multiple correlated regions that follow the plane distribution of the scene. Then, multiple local homography transformations, each of which aligns a correlated region precisely, are predicted and corresponding warped images are fused to obtain the final result. Furthermore, a new metric Mask-PSNR is proposed for more comprehensive evaluation of alignment. Extensive experiments are conducted to verify the effectiveness of the proposed method.

Modeling Adaptive Inter-Task Feature Interactions via Sentiment-Aware Contrastive Learning for Joint Aspect-Sentiment Prediction

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UniADS: Universal Architecture-Distiller Search for Distillation Gap

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Modeling Adaptive Inter-Task Feature Interactions via Sentiment-Aware Contrastive Learning for Joint Aspect-Sentiment Prediction

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UniADS: Universal Architecture-Distiller Search for Distillation Gap

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PRESENTATIONS

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RESOURCES

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