Classification

Video-Audio-Text Transformer, or VATT, is a framework for learning multimodal representations from unlabeled data using convolution-free Transformer architectures. Specifically, it takes raw signals as inputs and extracts multidimensional representations that are rich enough to benefit a variety of downstream tasks. VATT borrows the exact architecture from BERT and ViT except the layer of tokenization and linear projection reserved for each modality separately. The design follows the same spirit as ViT that makes the minimal changes to the architecture so that the learned model can transfer its weights to various frameworks and tasks. VATT linearly projects each modality into a feature vector and feeds it into a Transformer encoder. A semantically hierarchical common space is defined to account for the granularity of different modalities and noise contrastive estimation is employed to train the model.

k-Sparse Autoencoders are autoencoders with linear activation function, where in hidden layers only the $k$ highest activities are kept. This achieves exact sparsity in the hidden representation. Backpropagation only goes through the the top $k$ activated units. This can be achieved with a ReLU layer with an adjustable threshold.

Adaptive Early-Learning Correction for Segmentation from Noisy Annotations

CPVT, or Conditional Position Encoding Vision Transformer, is a type of vision transformer which utilizes conditional positional encoding. Other than the new encodings, it follows the same architecture of ViT and DeiT.

CTAL is a pre-training framework for strong audio-and-language representations with a Transformer, which aims to learn the intra-modality and inter-modalities connections between audio and language through two proxy tasks on a large amount of audio- and-language pairs: masked language modeling and masked cross-modal acoustic modeling. The pre-trained model is a Transformer for Audio and Language, i.e., CTAL, which consists of two modules, a language stream encoding module which adapts word as input element, and a text-referred audio stream encoder module which accepts both frame-level Mel-spectrograms and token-level output embeddings from the language stream

Deflation is a video-to-image operation to transform a video network into a network that can ingest a single image. In the two types of video networks considered in the original paper, this deflation corresponds to the following operations: for 3D convolutional based networks, summing the 3D spatio-temporal filters over the temporal dimension to obtain 2D filters; for TSM networks,, turning off the channel shifting which results in a standard residual architecture (ResNet50) for images.

Contrastive learning has achieved remarkable success in representation learning via self-supervision in unsupervised settings. However, effectively adapting contrastive learning to supervised learning tasks remains as a challenge in practice. In this work, we introduce a dual contrastive learning (DualCL) framework that simultaneously learns the features of input samples and the parameters of classifiers in the same space. Specifically, DualCL regards the parameters of the classifiers as augmented samples associating to different labels and then exploits the contrastive learning between the input samples and the augmented samples. Empirical studies on five benchmark text classification datasets and their low-resource version demonstrate the improvement in classification accuracy and confirm the capability of learning discriminative representations of DualCL.

Funnel Transformer is a type of Transformer that gradually compresses the sequence of hidden states to a shorter one and hence reduces the computation cost. By re-investing the saved FLOPs from length reduction in constructing a deeper or wider model, the model capacity is further improved. In addition, to perform token-level predictions as required by common pretraining objectives, Funnel-transformer is able to recover a deep representation for each token from the reduced hidden sequence via a decoder. The proposed model keeps the same overall skeleton of interleaved S-Attn and P-FFN sub-modules wrapped by residual connection and layer normalization. But differently, to achieve representation compression and computation reduction, THE model employs an encoder that gradually reduces the sequence length of the hidden states as the layer gets deeper. In addition, for tasks involving per-token predictions like pretraining, a simple decoder is used to reconstruct a full sequence of token-level representations from the compressed encoder output. Compression is achieved via a pooling operation,

Social media are nowadays one of the main news sources for millions of people around the globe due to their low cost, easy access and rapid dissemination. This however comes at the cost of dubious trustworthiness and significant risk of exposure to 'fake news', intentionally written to mislead the readers. Automatically detecting fake news poses challenges that defy existing content-based analysis approaches. One of the main reasons is that often the interpretation of the news requires the knowledge of political or social context or 'common sense', which current NLP algorithms are still missing. Recent studies have shown that fake and real news spread differently on social media, forming propagation patterns that could be harnessed for the automatic fake news detection. Propagation-based approaches have multiple advantages compared to their content-based counterparts, among which is language independence and better resilience to adversarial attacks. In this paper we show a novel automatic fake news detection model based on geometric deep learning. The underlying core algorithms are a generalization of classical CNNs to graphs, allowing the fusion of heterogeneous data such as content, user profile and activity, social graph, and news propagation. Our model was trained and tested on news stories, verified by professional fact-checking organizations, that were spread on Twitter. Our experiments indicate that social network structure and propagation are important features allowing highly accurate (92.7% ROC AUC) fake news detection. Second, we observe that fake news can be reliably detected at an early stage, after just a few hours of propagation. Third, we test the aging of our model on training and testing data separated in time. Our results point to the promise of propagation-based approaches for fake news detection as an alternative or complementary strategy to content-based approaches.

LTLS is a technique for multiclass and multilabel prediction that can perform training and inference in logarithmic time and space. LTLS embeds large classification problems into simple structured prediction problems and relies on efficient dynamic programming algorithms for inference. It tackles extreme multi-class and multi-label classification problems where the size $C$ of the output space is extremely large.

Multi-source Sentiment Generative Adversarial Network is a multi-source domain adaptation (MDA) method for visual sentiment classification. It is composed of three pipelines, i.e., image reconstruction, image translation, and cycle-reconstruction. To handle data from multiple source domains, it learns to find a unified sentiment latent space where data from both the source and target domains share a similar distribution. This is achieved via cycle consistent adversarial learning in an end-to-end manner. Notably, thanks to the unified sentiment latent space, MSGAN requires a single classification network to handle data from different source domains.

MinCutPool is a trainable pooling operator for graphs that learns to map nodes into clusters. The method is trained to approximate the minimum K-cut of the graph to ensure that the clusters are balanced, while also jointly optimizing the objective of the task at hand.

MultiGrain is a type of image model that learns a single embedding for classes, instances and copies. In other words, it is a convolutional neural network that is suitable for both image classification and instance retrieval. We learn MultiGrain by jointly training an image embedding for multiple tasks. The resulting representation is compact and can outperform narrowly-trained embeddings. The learned embedding output incorporates different levels of granularity.

Sparse Sinkhorn Attention is an attention mechanism that reduces the memory complexity of the dot-product attention mechanism and is capable of learning sparse attention outputs. It is based on the idea of differentiable sorting of internal representations within the self-attention module. SSA incorporates a meta sorting network that learns to rearrange and sort input sequences. Sinkhorn normalization is used to normalize the rows and columns of the sorting matrix. The actual SSA attention mechanism then acts on the block sorted sequences.

Domain-Symmetric Network, or SymmNet, is an algorithm for unsupervised multi-class domain adaptation. It features an adversarial strategy of domain confusion and discrimination.

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Aggregated Learning (AgrLearn) is a vector-quantization approach to learning neural network classifiers. It builds on an equivalence between IB learning and IB quantization and exploits the power of vector quantization, which is well known in information theory.

The BP-Transformer (BPT) is a type of Transformer that is motivated by the need to find a better balance between capability and computational complexity for self-attention. The architecture partitions the input sequence into different multi-scale spans via binary partitioning (BP). It incorporates an inductive bias of attending the context information from fine-grain to coarse-grain as the relative distance increases. The farther the context information is, the coarser its representation is. BPT can be regard as graph neural network, whose nodes are the multi-scale spans. A token node can attend the smaller-scale span for the closer context and the larger-scale span for the longer distance context. The representations of nodes are updated with Graph Self-Attention.

TL;DR: CT-Layer is a GNN Layer which is able to rewire a graph in an inductive an parameter-free way according to the commute times distance (or effective resistance). We address it learning a differentiable way to compute the CT-embedding of the graph. ### Summary CT-Layer is able to Learn the Commute Times distance between nodes (i.e. effective resistance distance) in a differentiable way, instead of the common spectral version, and in a parameter free manner, which is not the cased of the heat kernel. This approach allow to solve it as an optimization problem inside a GNN, leading to have a new layer which is able to learn how rewire a given graph in an optimal, and inductive way. In addition, CT-Layer also is able to learn Commute Times embeddings, and then calculate it for any graph in an inductive way. The Commute Times embedding is also related with the eigenvalues and eigenvectors of the Laplacian of the graph, because CT embedding is just the eigenvectors scaled. Therefore, CT-Layer is also able to learn hot to calculate the spectrum of the Laplacian in a differentiable way. Therefore, this embedding must satisfy orthogonality and normality. Finally, recent connections has been found between commute times distance and curvature (which is non-differentiable), establishing equivalences between them. Therefore, CT-Layer can also be seen as the differentiable version of the curvature rewiring. **We are going through a quick overview of the layer, but I suggest go to the paper for a detailed explanation. ** ### Spectral CT- Embedding downsides CT-embedding $\mathbf{Z}$ is computed spectrally in the literature (until the proposal of this method) or it is approximated using the heat kernel (very dependent on hyperparameter $t$). This fact does not allow us to propose differentiable methods using that measure: $$ \mathbf{Z}=\sqrt{vol(G)}\mathbf{\Lambda}^\frac{1}{2}\mathbf{F}^T \textrm{ given } \mathbf{L}=\mathbf{F}\mathbf{\Lambda}\mathbf{F}^T $$ Then, CT-distance is given by the Euclidean distances between the embeddings $CT_{uv} = ||\mathbf{z_u}-\mathbf{z_v}||^2$. The spectral form is: $$ \frac{CT_{uv}}{vol(G)} = \sum_{i=2}^n \frac{1}{\lambda_i} (\mathbf{f}(u)-\mathbf{f}(v))^2 $$ being $\mathbf{f}$ the eigenvectors of the graph Laplacian. This embedding and distances gives us desirable properties of the graph, such an understanding of the structure, or an embedding based on the spectrum which minimizes Dirichlet energies. However, the spectral computation is not differentiable. ### CT-Layer as an optimization problem: Differentiable, learnable and inductive CT-Layer Giving that $\mathbf{Z}$ minimizes Dirichlet energies s.t. being orthogonal and normalized, we can formulate this problem as constraining neighboring nodes to have a similar embeddings s.t. $\mathbf{Z}\mathbf{Z}^T=\mathbf{I}$. $$ \mathbf{Z} = \arg\min_{\mathbf{Z}^T\mathbf{Z}=\mathbf{I}} \frac{\sum\_{u,v} ||\mathbf{z_u}-\mathbf{z_v}||^2\mathbf{A}\_{uv}}{\sum\_{u,v} \mathbf{Z}^2\_{uv} d_u}=\frac{Tr[\mathbf{Z}^T\mathbf{L}\mathbf{Z}]}{Tr[\mathbf{Z}^T\mathbf{D}\mathbf{Z}]} $$ With the above elements we have a definition of CT-Layer, our rewiring layer: Given the matrix $\mathbf{X}\_{n\times F}$ encoding the features of the nodes after any message passing (MP) layer, $\mathbf{Z}\_{n\times O(n)}=\tanh(\textrm{MLP}(\mathbf{X}))$ learns the association $\mathbf{X}\rightarrow \mathbf{Z}$ while $\mathbf{Z}$ is optimized according to the loss $$ L\_{CT} = \frac{Tr[\mathbf{Z}^T\mathbf{L}\mathbf{Z}]}{Tr[\mathbf{Z}^T\mathbf{D}\mathbf{Z}]} + \left\|\frac{\mathbf{Z}^T\mathbf{Z}}{\|\mathbf{Z}^T\mathbf{Z}\|\_F} - \mathbf{I}\_n\right\|\_F $$ This results in the following resistance diffusion $\mathbf{T}^{CT} = \mathbf{R}(\mathbf{S})\odot \mathbf{A}$ (Hadamard product between the resistance distance and the adjacency) which provides as input to the subsequent MP layer a learnt convolution matrix. As explained before, $\mathbf{Z}$ is the commute times embedding matrix and the pairwise euclidian distance of that learned embeddings are the commute times distances or resistance distances. Therefore, once trained this layer, it will be able to calculate the commute times embedding for a new graph, and rewire that new and unseen graph in a principled way based on the commute times distance. ## Preservation of Structure Does this rewiring preserve the original structure? Let $G' = \textrm{Sparsify}(G, q)$ be a sampling algorithm of graph $G = (V, E)$, where edges $e \in E$ are sampled with probability $q\propto R_e$ (proportional to the effective resistance, i.e. commute times). Then, for $n = |V|$ sufficiently large and $1/\sqrt{n}< \epsilon\le 1$, we need O(n\log n/\epsilon^2)$ samples to satisfy: $$ \forall \mathbf{x}\in\mathbb{R}^n:\; (1-\epsilon)\mathbf{x}^T\mathbf{L}\_G\mathbf{x}\le\mathbf{x}^T\mathbf{L}\_{G'}\mathbf{x}\le (1+\epsilon)\mathbf{x}^T\mathbf{L}\_G\mathbf{x} $$ The intuitions behind is that Dirichlet energies in $G'$ are bounded in $(1\pm \epsilon)$ of the Dirichlet energies of the original graph $G$.

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