Temporal Difference Learning for High-Dimensional PIDEs with Jumps

In this paper, we propose a deep learning framework for solving high-dimensional partial integro-differential equations (PIDEs) based on the temporal difference learning. We introduce a set of Levy processes and construct a corresponding reinforcement learning model. To simulate the entire process, we use deep neural networks to represent the solutions and non-local terms of the equations. Subsequently, we train the networks using the temporal difference error, termination condition, and properties of the non-local terms as the loss function. The relative error of the method reaches O(10^{-3}) in 100-dimensional experiments and O(10^{-4}) in one-dimensional pure jump problems. Additionally, our method demonstrates the advantages of low computational cost and robustness, making it well-suited for addressing problems with different forms and intensities of jumps.

Genes in Intelligent Agents

Training intelligent agents in Reinforcement Learning (RL) is much more time-consuming than animal learning. This is because agents learn from scratch, but animals learn with genes inherited from ancestors and are born with some innate abilities. Inspired by genes in animals, here we conceptualize the gene in intelligent agents and introduce Genetic Reinforcement Learning (GRL), a computational framework to represent, evaluate, and evolve genes (in agents). Leveraging GRL we identify genes and demonstrate several advantages of genes. First, we find that genes take the form of the fragment of agents' neural networks and can be inherited across generations. Second, we validate that genes bring better and stabler learning ability to agents, since genes condense knowledge from ancestors and bring agent with innate abilities. Third, we present evidence of Lamarckian evolution in intelligent agents. The continuous encoding of knowledge into genes across generations facilitates the evolution of genes. Overall, our work promotes a novel paradigm to train agents by incorporating genes.

Exploring Diverse In-Context Configurations for Image Captioning

After discovering that Language Models (LMs) can be good in-context few-shot learners, numerous strategies have been proposed to optimize in-context sequence configurations. Recently, researchers in Vision-Language (VL) domains also develop their few-shot learners, while they only use the simplest way, i.e., randomly sampling, to configure in-context image-text pairs. In order to explore the effects of varying configurations on VL in-context learning, we devised four strategies for image selection and four for caption assignment to configure in-context image-text pairs for image captioning. Here Image Captioning is used as the case study since it can be seen as the visually-conditioned LM. Our comprehensive experiments yield two counter-intuitive but valuable insights, highlighting the distinct characteristics of VL in-context learning due to multi-modal synergy, as compared to the NLP case.

Transforming Visual Scene Graphs to Image Captions

We propose to Transform Scene Graphs (TSG) into more descriptive captions. In TSG, we apply multi-head attention (MHA) to design the Graph Neural Network (GNN) for embedding scene graphs. After embedding, different graph embeddings contain diverse specific knowledge for generating the words with different part-of-speech, e.g., object/attribute embedding is good for generating nouns/adjectives. Motivated by this, we design a Mixture-of-Expert (MOE)-based decoder, where each expert is built on MHA, for discriminating the graph embeddings to generate different kinds of words. Since both the encoder and decoder are built based on the MHA, as a result, we construct a homogeneous encoder-decoder unlike the previous heterogeneous ones which usually apply Fully-Connected-based GNN and LSTM-based decoder. The homogeneous architecture enables us to unify the training configuration of the whole model instead of specifying different training strategies for diverse sub-networks as in the heterogeneous pipeline, which releases the training difficulty. Extensive experiments on the MS-COCO captioning benchmark validate the effectiveness of our TSG. The code is in: https://anonymous.4open.science/r/ACL23_TSG.

Learngene: Inheriting Condensed Knowledge from the Ancestry Model to Descendant Models

During the continuous evolution of one organism's ancestry, its genes accumulate extensive experiences and knowledge, enabling newborn descendants to rapidly adapt to their specific environments. Motivated by this observation, we propose a novel machine learning paradigm \textit{Learngene} to enable learning models to incorporate three key characteristics of genes. (i) Accumulating: the knowledge is accumulated during the continuous learning of an \textbf{ancestry model}. (ii) Condensing: the exhaustive accumulated knowledge is condensed into a much more compact information piece, \ie \textbf{learngene}. (iii): Inheriting: the condensed \textbf{learngene} is inherited to make it easier for \textbf{descendant models} to adapt to new environments. Since accumulating has been studied in some well-developed paradigms like large-scale pre-training and lifelong learning, we focus on condensing and inheriting, which induces three key issues and we provide the preliminary solutions to these issues in this paper: (i) \textit{Learngene} Form: the \textbf{learngene} is set to a few integral layers that can preserve the most commonality. (ii) \textit{Learngene} Condensing: we identify which layers among the ancestry model have the most similarity as one pseudo descendant model. (iii) \textit{Learngene} Inheriting: to construct distinct descendant models for specific downstream tasks, we stack some randomly initialized layers to the \textbf{learngene} layers. Extensive experiments of various settings, including using different network architectures like Vision Transformer (ViT) and Convolutional Neural Networks (CNNs) on different datasets, are carried out to confirm five advantages and two characteristics of \textit{Learngene}.

SC-ML: Self-supervised Counterfactual Metric Learning for Debiased Visual Question Answering

Visual question answering (VQA) is a critical multimodal task in which an agent must answer questions according to the visual cue. Unfortunately, language bias is a common problem in VQA, which refers to the model generating answers only by associating with the questions while ignoring the visual content, resulting in biased results. We tackle the language bias problem by proposing a self-supervised counterfactual metric learning (SC-ML) method to focus the image features better. SC-ML can adaptively select the question-relevant visual features to answer the question, reducing the negative influence of question-irrelevant visual features on inferring answers. In addition, question-irrelevant visual features can be seamlessly incorporated into counterfactual training schemes to further boost robustness. Extensive experiments have proved the effectiveness of our method with improved results on the VQA-CP dataset. Our code will be made publicly available.

Spatial Attention and Syntax Rule Enhanced Tree Decoder for Offine Handwritten Mathematical Expression Recognition

Offline Handwritten Mathematical Expression Recognition (HMER) has been dramatically advanced recently by employing tree decoders as part of the encoder-decoder method. Despite the tree decoder-based methods regard the expressions as a tree and parse 2D spatial structure to the tree nodes sequence, the performance of existing works is still poor due to the inevitable tree nodes prediction errors. Besides, they lack syntax rules to regulate the output of expressions. In this paper, we propose a novel model called Spatial Attention and Syntax Rule Enhanced Tree Decoder (SS-TD), which is equipped with spatial attention mechanism to alleviate the prediction error of tree structure and use syntax masks (obtained from the transformation of syntax rules) to constrain the occurrence of ungrammatical mathematical expression. In this way, our model can effectively describe tree structure and increase the accuracy of output expression. Experiments show that SS-TD achieves better recognition performance than prior models on CROHME 14/16/19 datasets, demonstrating the effectiveness of our model.

Learning Trajectory-Word Alignments for Video-Language Tasks

Aligning objects with words plays a critical role in Image-Language BERT (IL-BERT) and Video-Language BERT (VDL-BERT). Different from the image case where an object covers some spatial patches, an object in a video usually appears as an object trajectory, i.e., it spans over a few spatial but longer temporal patches and thus contains abundant spatiotemporal contexts. However, modern VDL-BERTs neglect this trajectory characteristic that they usually follow IL-BERTs to deploy the patch-to-word (P2W) attention while such attention may over-exploit trivial spatial contexts and neglect significant temporal contexts. To amend this, we propose a novel TW-BERT to learn Trajectory-Word alignment for solving video-language tasks. Such alignment is learned by a newly designed trajectory-to-word (T2W) attention. Besides T2W attention, we also follow previous VDL-BERTs to set a word-to-patch (W2P) attention in the cross-modal encoder. Since T2W and W2P attentions have diverse structures, our cross-modal encoder is asymmetric. To further help this asymmetric cross-modal encoder build robust vision-language associations, we propose a fine-grained ``align-before-fuse'' strategy to pull close the embedding spaces calculated by the video and text encoders. By the proposed strategy and T2W attention, our TW-BERT achieves SOTA performances on text-to-video retrieval tasks, and comparable performances on video question answering tasks with some VDL-BERTs trained on much more data. The code will be available in the supplementary material.

Adaptively Clustering Neighbor Elements for Image Captioning

We design a novel global-local Transformer named \textbf{Ada-ClustFormer} (\textbf{ACF}) to generate captions. We use this name since each layer of ACF can adaptively cluster input elements to carry self-attention (Self-ATT) for learning local context. Compared with other global-local Transformers which carry Self-ATT in fixed-size windows, ACF can capture varying graininess, \eg, an object may cover different numbers of grids or a phrase may contain diverse numbers of words. To build ACF, we insert a probabilistic matrix C into the Self-ATT layer. For an input sequence {{s}_1,...,{s}_N , C_{i,j} softly determines whether the sub-sequence {s_i,...,s_j} should be clustered for carrying Self-ATT. For implementation, {C}_{i,j} is calculated from the contexts of {{s}_i,...,{s}_j}, thus ACF can exploit the input itself to decide which local contexts should be learned. By using ACF to build the vision encoder and language decoder, the captioning model can automatically discover the hidden structures in both vision and language, which encourages the model to learn a unified structural space for transferring more structural commonalities. The experiment results demonstrate the effectiveness of ACF that we achieve CIDEr of 137.8, which outperforms most SOTA captioning models and achieve comparable scores compared with some BERT-based models. The code will be available in the supplementary material.