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An aspect of Bi-RNNs that could be undesirable is the architecture's symmetry in both time directions. Bi-RNNs are often used in natural language processing, where the order of the words is almost exclusively determined by grammatical rules and not by temporal sequentiality. However, in some cases, the data has a preferred direction in time: the forward direction. Another potential drawback of Bi-RNNs is that their output is simply the concatenation of two naive readings of the input in both directions. In consequence, Bi-RNNs never actually read an input by knowing what happens in the future. Conversely, the idea behind U-RNN, is to first do a backward pass, and then use during the forward pass information about the future. We accumulate information while knowing which part of the information will be useful in the future as it should be relevant to do so if the forward direction is the preferred direction of the data. The backward and forward hidden states $(h^b_t)$ and $(h^f_t)$ are obtained according to these equations: \begin{equation} \begin{aligned} &h_{t-1}^{b}=R N N\left(h_{t}^{b}, e_{t}, W_{b}\right) \ &h_{t+1}^{f}=R N N\left(h_{t}^{f},\left[e_{t}, h_{t}^{b}\right], W_{f}\right) \end{aligned} \end{equation} where $W_b$ and $W_f$ are learnable weights that are shared among pedestrians, and $[\cdot, \cdot]$ denotes concatenation. The last hidden state $h^f_{T_{obs}}$ is then used as the encoding of the sequence.

VLG-Net leverages recent advantages in Graph Neural Networks (GCNs) and leverages a novel multi-modality graph-based fusion method for the task of natural language video grounding.

Viewmaker Network is a type of generative model that learns to produce input-dependent views for contrastive learning. This network is trained jointly with an encoder network. The viewmaker network is trained adversarially to create views which increase the contrastive loss of the encoder network. Rather than directly outputting views for an image, the viewmaker instead outputs a stochastic perturbation that is added to the input. This perturbation is projected onto an $\mathcal{l}\_{p}$ sphere, controlling the effective strength of the view, similar to methods in adversarial robustness. This constrained adversarial training method enables the model to reduce the mutual information between different views while preserving useful input features for the encoder to learn from. Specifically, the encoder and viewmaker are optimized in alternating steps to minimize and maximize $\mathcal{L}$, respectively. An image-to-image neural network is used as the viewmaker network, with an architecture adapted from work on style transfer. This network ingests the input image and outputs a perturbation that is constrained to an $\ell_{1}$ sphere. The sphere's radius is determined by the volume of the input tensor times a hyperparameter $\epsilon$, the distortion budget, which determines the strength of the applied perturbation. This perturbation is added to the input image and optionally clamped in the case of images to ensure all pixels are in $[0,1]$.

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YOLOP is a panoptic driving perception network for handling traffic object detection, drivable area segmentation and lane detection simultaneously. It is composed of one encoder for feature extraction and three decoders to handle the specific tasks. It can be thought of a lightweight version of Tesla's HydraNet model for self-driving cars. A lightweight CNN, from Scaled-yolov4, is used as the encoder to extract features from the image. Then these feature maps are fed to three decoders to complete their respective tasks. The detection decoder is based on the current best-performing single-stage detection network, YOLOv4, for two main reasons: (1) The single-stage detection network is faster than the two-stage detection network. (2) The grid-based prediction mechanism of the single-stage detector is more related to the other two semantic segmentation tasks, while instance segmentation is usually combined with the region based detector as in Mask R-CNN. The feature map output by the encoder incorporates semantic features of different levels and scales, and our segmentation branch can use these feature maps to complete pixel-wise semantic prediction.

ZeRO-Infinity is a sharded data parallel system that extends ZeRO with new innovations in heterogeneous memory access called the infinity offload engine. This allows ZeRO-Infinity to support massive model sizes on limited GPU resources by exploiting CPU and NVMe memory simultaneously. In addition, ZeRO-Infinity also introduces a novel GPU memory optimization technique called memory-centric tiling to support extremely large individual layers that would otherwise not fit in GPU memory even one layer at a time.

We introduce myGym, a toolkit suitable for fast prototyping of neural networks in the area of robotic manipulation and navigation. Our toolbox is fully modular, enabling users to train their algorithms on different robots, environments, and tasks. We also include pretrained neural network modules for the real-time vision that allows training visuomotor tasks with sim2real transfer. The visual modules can be easily retrained using the dataset generation pipeline with domain augmentation and randomization. Moreover, myGym provides automatic evaluation methods and baselines that help the user to directly compare their trained model with the state-of-the-art algorithms. We additionally present a novel metric, called learnability, to compare the general learning capability of algorithms in different settings, where the complexity of the environment, robot, and the task is systematically manipulated. The learnability score tracks differences between the performance of algorithms in increasingly challenging setup conditions, and thus allows the user to compare different models in a more systematic fashion. The code is accessible at https://github.com/incognite-lab/myGym

Pixel2Style2Pixel, or pSp, is an image-to-image translation framework that is based on a novel encoder that directly generates a series of style vectors which are fed into a pretrained StyleGAN generator, forming the extended $\mathcal{W+}$ latent space. Feature maps are first extracted using a standard feature pyramid over a ResNet backbone. Then, for each of $18$ target styles, a small mapping network is trained to extract the learned styles from the corresponding feature map, where styles $(0-2)$ are generated from the small feature map, $(3-6)$ from the medium feature map, and $(7-18)$ from the largest feature map. The mapping network, map2style, is a small fully convolutional network, which gradually reduces spatial size using a set of 2-strided convolutions followed by LeakyReLU activations. Each generated 512 vector, is fed into StyleGAN, starting from its matching affine transformation, $A$.

The time-causal and time-recursive scale-space representation is obtained by filtering any 1-D signal with the time-causal limit kernel, and provides a way to define a multi-scale analysis for signals, for which the future cannot be accessed and additionally the computations should be strictly time-recursive, in order to not require any complementary memory of the past beyond the temporal scale-space representation itself.

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3DSSD is a point-based 3D single stage object detection detector. In this paradigm, all upsampling layers and refinement stage, which are indispensable in all existing point-based methods, are abandoned to reduce the large computation cost. The authors propose a fusion sampling strategy in the downsampling process to make detection on less representative points feasible. A delicate box prediction network including a candidate generation layer, an anchor-free regression head with a 3D center-ness assignment strategy is designed to meet the needs of accuracy and speed.

The aim of 4D A* is to find the shortest path between two four-dimensional (4D) nodes of a 4D search space - a starting node and a target node - as long as there is a path. It achieves both optimality and completeness. The former is because the path is shortest possible, and the latter because if the solution exists the algorithm is guaranteed to find it.

A CNN-based network that uses the ANDHRA module to split the network into multiple parallel branches, leading to multiple network outputs. For a network with three ANDHRA modules with a branching factor of two, the total output heads are 2ˆ3 = 8.

Adaptive Dynamic Recursive Mapping (ADRM) $$ s_{t+1}=\psi_{\alpha}(s_t)=s_t+\alpha\,(s_t-s_t^{2}),\quad \alpha\in[-1,1]. $$ - $\alpha>0$ amplifies evidence for abnormality - $\alpha<0$ suppresses false positives - $\alpha=0$ leaves the score unchanged Here, $s_t$ is the anomaly score at step $t$, and the adaptive decision parameter $\alpha$ is learned jointly with the backbone detector. By recursively mapping the score trajectory, ADRM stabilises detector outputs, magnifies truly anomalous segments, and damps noisy spikes, yielding more reliable video-level anomaly detection under weak supervision and heterogeneous federated settings.

The Adaptive Parametric activation (APA) is defined as: $APA(z,λ,κ) = (λ exp(−κz) + 1) ^{\frac{1}{−λ}}$, where $λ$ and $κ$ are learnable parameters. This activation function is a generalisation of the Sigmoid and the Gumbel activation functions and it is expressive and versatile. For example, APA can be used inside the channel attention mechanism instead of the Sigmoid activation, or it can be used inside the intermediate layers using the Adaptive Generalised Linear Unit (AGLU): $AGLU(z,λ,κ) = z APA(z,λ,κ)$.

An infinite image generator which is based on a patch-wise, periodically equivariant generator.

ALP-GMM is is an algorithm that learns to generate a learning curriculum for black box reinforcement learning agents, whereby it sequentially samples parameters controlling a stochastic procedural generation of tasks or environments.

Many communication-efficient variants of SGD use gradient quantization schemes. These schemes are often heuristic and fixed over the course of training. We empirically observe that the statistics of gradients of deep models change during the training. Motivated by this observation, we introduce two adaptive quantization schemes, ALQ and AMQ. In both schemes, processors update their compression schemes in parallel by efficiently computing sufficient statistics of a parametric distribution. We improve the validation accuracy by almost 2% on CIFAR-10 and 1% on ImageNet in challenging low-cost communication setups. Our adaptive methods are also significantly more robust to the choice of hyperparameters.

AMSBound is a variant of the AMSGrad stochastic optimizer which is designed to be more robust to extreme learning rates. Dynamic bounds are employed on learning rates, where the lower and upper bound are initialized as zero and infinity respectively, and they both smoothly converge to a constant final step size. AMSBound can be regarded as an adaptive method at the beginning of training, and it gradually and smoothly transforms to SGD (or with momentum) as time step increases. $$ g\_{t} = \nabla{f}\_{t}\left(x\_{t}\right) $$ $$ m\_{t} = \beta\_{1t}m\_{t-1} + \left(1-\beta\_{1t}\right)g\_{t} $$ $$ v\_{t} = \beta\_{2}v\_{t-1} + \left(1-\beta\_{2}\right)g\_{t}^{2}$$ $$ \hat{v}\_{t} = \max\left(\hat{v}\_{t-1}, v\_{t}\right) \text{ and } V\_{t} = \text{diag}\left(\hat{v}\_{t}\right) $$ $$ \eta = \text{Clip}\left(\alpha/\sqrt{V\_{t}}, \eta\_{l}\left(t\right), \eta\_{u}\left(t\right)\right) \text{ and } \eta\_{t} = \eta/\sqrt{t} $$ $$ x\_{t+1} = \Pi\_{\mathcal{F}, \text{diag}\left(\eta\_{t}^{-1}\right)}\left(x\_{t} - \eta\_{t} \odot m\_{t} \right) $$ Where $\alpha$ is the initial step size, and $\eta_{l}$ and $\eta_{u}$ are the lower and upper bound functions respectively.

ANDHRA module splits the neural network into multiple “branches” (representing different paths) that process the same input signal in parallel, each corresponding to different possible outcomes.

ARShoe is a multi-branch network for pose estimation and segmentation tackling the "try-on" problem for augmented reality shoes. Consisting of an encoder and a decoder, the multi-branch network is trained to predict keypoints heatmap (heatmap), PAFs heatmap (pafmap), and segmentation results (segmap) simultaneously. Post processes are then performed for a smooth and realistic virtual try-on.

ASLFeat is a convolutional neural network for learning local features that uses deformable convolutional networks to densely estimate and apply local transformation. It also takes advantage of the inherent feature hierarchy to restore spatial resolution and low-level details for accurate keypoint localization. Finally, it uses a peakiness measurement to relate feature responses and derive more indicative detection scores.

The Auditory Cortex ResNet, briefly AUCO ResNet, is proposed and tested. It is a deep neural network architecture especially designed for audio classification trained end-to-end. It is inspired by the architectural organization of rat's auditory cortex, containing also innovations 2 and 3. The network outperforms the state-of-the-art accuracies on a reference audio benchmark dataset without any kind of preprocessing, imbalanced data handling and, most importantly, any kind of data augmentation.

Audiovisual SlowFast Network, or AVSlowFast, is an architecture for integrated audiovisual perception. AVSlowFast has Slow and Fast visual pathways that are integrated with a Faster Audio pathway to model vision and sound in a unified representation. Audio and visual features are fused at multiple layers, enabling audio to contribute to the formation of hierarchical audiovisual concepts. To overcome training difficulties that arise from different learning dynamics for audio and visual modalities, DropPathway is used, which randomly drops the Audio pathway during training as an effective regularization technique. Inspired by prior studies in neuroscience, hierarchical audiovisual synchronization is performed to learn joint audiovisual features.

ADACT: LEARNING TO OPTIMIZE ACTIVATION FUNCTION CHOICE THROUGH ADAPTIVE ACTIVATION MODULES This paper presents an innovative approach to enhancing neural network performance through the development and implementation of an adaptive activation function, termed adaptive activation (AdAct). AdAct amalgamates various wellestablished and novel activation functions into a single, learnable framework, allowing dynamic adaptation to specific network layers’ needs. We explore the effectiveness of ReLU and its variants, including ELU, LReLU, PReLU, RReLU, and more recent functions like Swish and Mish, integrating them into the AdAct function. Employing ConvNet variants across FMNIST, CIFAR10, SVHN and FER datasets, our study empirically assesses each function’s contribution and demonstrates AdAct’s potential in optimizing neural networks, especially in selecting optimal activation functions for diverse tasks.

AdaGPR is an adaptive, layer-wise graph convolution model. AdaGPR applies adaptive generalized Pageranks at each layer of a GCNII model by learning to predict the coefficients of generalized Pageranks using sparse solvers.

AdaMod is a stochastic optimizer that restricts adaptive learning rates with adaptive and momental upper bounds. The dynamic learning rate bounds are based on the exponential moving averages of the adaptive learning rates themselves, which smooth out unexpected large learning rates and stabilize the training of deep neural networks. The weight updates are performed as: $$ g\_{t} = \nabla{f}\_{t}\left(\theta\_{t-1}\right) $$ $$ m\_{t} = \beta\_{1}m\_{t-1} + \left(1-\beta\_{1}\right)g\_{t} $$ $$ v\_{t} = \beta\_{2}v\_{t-1} + \left(1-\beta\_{2}\right)g\_{t}^{2} $$ $$ \hat{m}\_{t} = m\_{t} / \left(1 - \beta^{t}\_{1}\right)$$ $$ \hat{v}\_{t} = v\_{t} / \left(1 - \beta^{t}\_{2}\right)$$ $$ \eta\_{t} = \alpha\_{t} / \left(\sqrt{\hat{v}\_{t}} + \epsilon\right) $$ $$ s\_{t} = \beta\_{3}s\_{t-1} + (1-\beta\_{3})\eta\_{t} $$ $$ \hat{\eta}\_{t} = \text{min}\left(\eta\_{t}, s\_{t}\right) $$ $$ \theta\_{t} = \theta\_{t-1} - \hat{\eta}\_{t}\hat{m}\_{t} $$

AdaRNN is an adaptive RNN that learns an adaptive model through two modules: Temporal Distribution Characterization (TDC) and Temporal Distribution Matching (TDM) algorithms. Firstly, to better characterize the distribution information in time-series, TDC splits the training data into $K$ most diverse periods that have a large distribution gap inspired by the principle of maximum entropy. After that, a temporal distribution matching (TDM) algorithm is used to dynamically reduce distribution divergence using a RNN-based model.