In addition, according to a recent theorem #REFR that capturing uncertainty information and correcting overconfidence can be achieved by making only the last layer of a model in binary classification, we can assume that adding a GP layer is Bayesian enough so that GPF-BERT can achieve better calibration.

"↑" represents higher is better and "↓" means lower is better.

All the models are trained in one dataset and test in the other dataset.

ble, GPF-BERT has a significant decrease (at least 8 times) in inference time.

While not completely free, GPF-BERT only adds negligible computational cost, but it greatly improves the calibration, which facilitates adaptation to other models.

We believe that GP maintains a distribution over functions rather than model parameters, which enables GPF-BERT to improve uncertainty calibration for dialog response retrieval models.

Efficient Uncertainty Estimation With Gaussian Process For Reliable Dialog Response Retrieval

Abstract: Deep neural networks have achieved remarkable performance in retrieval-based dialogue systems, but they are shown to be ill calibrated. Though basic calibration methods like Monte Carlo Dropout and Ensemble can calibrate well, these methods are time-consuming in the training or inference stages. To tackle these challenges, we propose an efficient uncertainty calibration framework GPF-BERT for BERT-based conversational search, which employs a Gaussian Process layer and the focal loss on top of the BERT architecture to achieve a highquality neural ranker. Extensive experiments are conducted to verify the effectiveness of our method. In comparison with basic calibration methods, GPF-BERT achieves the lowest empirical calibration error (ECE) in three in-domain datasets and the distributional shift tasks, while yielding the highest R 10 @1 and MAP performance on most cases. In terms of time consumption, our GPF-BERT has an 8× speedup.

Being Bayesian, Even Just A Bit, Fixes Overconfidence In Relu Networks

Abstract: The point estimates of ReLU classification networks---arguably the most widely used neural network architecture---have been shown to yield arbitrarily high confidence far away from the training data. This architecture, in conjunction with a maximum a posteriori estimation scheme, is thus not calibrated nor robust. Approximate Bayesian inference has been empirically demonstrated to improve predictive uncertainty in neural networks, although the theoretical analysis of such Bayesian approximations is limited. We theoretically analyze approximate Gaussian posterior distributions on the weights of ReLU networks and show that they fix the overconfidence problem. Furthermore, we show that even a simplistic, thus cheap, Bayesian approximation, also fixes these issues. This indicates that a sufficient condition for a calibrated uncertainty on a ReLU network is ``to be a bit Bayesian''. These theoretical results validate the usage of last-layer Bayesian approximation and motivate a range of a fidelity-cost trade-off. We further validate these findings empirically via various standard experiments using common deep ReLU networks and Laplace approximations.


Sim-CLR #REFR utilizes augmented images as positives while the other images in the same batch as negatives.

The fundamental idea of contrastive learning is to minimize the distance between similar (i.e., positive) instances while maximizing the distance among dissimilar (i.e., negative) instances #OTHEREFR .

Maintaining a similar contrastive concept, MoCo #OTHEREFR exploits a momentum encoder and proposes a dynamic dictionary with a queue to handle negative samples efficiently in both performance and memory perspectives. InfoNCE loss #OTHEREFR is often used in contrastive learning.

Minimizing this loss increases mutual information between positive pairs so that the model can extract the consistent features between the original and augmented samples.

Dualfair: Fair Representation Learning At Both Group And Individual Levels Via Contrastive Self-Supervision

Abstract: Algorithmic fairness has become an important machine learning problem, especially for mission-critical Web applications. This work presents a self-supervised model, called DualFair, that can debias sensitive attributes like gender and race from learned representations. Unlike existing models that target a single type of fairness, our model jointly optimizes for two fairness criteria-group fairness and counterfactual fairness-and hence makes fairer predictions at both the group and individual levels. Our model uses contrastive loss to generate embeddings that are indistinguishable for each protected group, while forcing the embeddings of counterfactual pairs to be similar. It then uses a self-knowledge distillation method to maintain the quality of representation for the downstream tasks. Extensive analysis over multiple datasets confirms the model's validity and further shows the synergy of jointly addressing two fairness criteria, suggesting the model's potential value in fair intelligent Web applications. CCS CONCEPTS • Computing methodologies → Philosophical/theoretical foundations of artificial intelligence; Machine learning.

A Simple Framework For Contrastive Learning Of Visual Representations

Abstract: This paper presents SimCLR: a simple framework for contrastive learning of visual representations. We simplify recently proposed contrastive self-supervised learning algorithms without requiring specialized architectures or a memory bank. In order to understand what enables the contrastive prediction tasks to learn useful representations, we systematically study the major components of our framework. We show that (1) composition of data augmentations plays a critical role in defining effective predictive tasks, (2) introducing a learnable nonlinear transformation between the representation and the contrastive loss substantially improves the quality of the learned representations, and (3) contrastive learning benefits from larger batch sizes and more training steps compared to supervised learning. By combining these findings, we are able to considerably outperform previous methods for self-supervised and semi-supervised learning on ImageNet. A linear classifier trained on self-supervised representations learned by SimCLR achieves 76.5% top-1 accuracy, which is a 7% relative improvement over previous state-of-the-art, matching the performance of a supervised ResNet-50. When fine-tuned on only 1% of the labels, we achieve 85.8% top-5 accuracy, outperforming AlexNet with 100X fewer labels.


Our sparsification strategy is also distinct from previous works that uniformly remove pixels through the entire depth range #REFR since we sparsify the label by restricting the observable depth range during the training.

In many depth estimation datasets, ground truth (GT) depth labels are sparsely annotated due to the hardware limitation of depth sensors, such as LiDAR, Radar, Structured-Light, and Time-of-Flight #OTHEREFR You et al., 2019b] .

To better utilize this sparse information, approaches to incorporate sparse GT as additional input have been proposed #OTHEREFR .

This task, also known as depth completion, differs from MDE because an additional sparse depth map is used as an input to supplement RGB information.

In our proposed MDE setup, we also sparsify the depth map but it is only used as a label (not input) in the training process.

When compared to previously studied sparse setups, the proposed setup is far more challenging because the distribution of GT labels significantly differs for training and test phases.

Depth-Relative Self Attention For Monocular Depth Estimation

Abstract: Monocular depth estimation is very challenging because clues to the exact depth are incomplete in a single RGB image. To overcome the limitation, deep neural networks rely on various visual hints such as size, shade, and texture extracted from RGB information. However, we observe that if such hints are overly exploited, the network can be biased on RGB information without considering the comprehensive view. We propose a novel depth estimation model named RElative Depth Transformer (RED-T) that uses relative depth as guidance in selfattention. Specifically, the model assigns high attention weights to pixels of close depth and low attention weights to pixels of distant depth. As a result, the features of similar depth can become more likely to each other and thus less prone to misused visual hints. We show that the proposed model achieves competitive results in monocular depth estimation benchmarks and is less biased to RGB information. In addition, we propose a novel monocular depth estimation benchmark that limits the observable depth range during training in order to evaluate the robustness of the model for unseen depths.

Estimating Depth From Rgb And Sparse Sensing

Abstract: We present a deep model that can accurately produce dense depth maps given an RGB image with known depth at a very sparse set of pixels. The model works simultaneously for both indoor/outdoor scenes and produces state-of-the-art dense depth maps at nearly real-time speeds on both the NYUv2 and KITTI datasets. We surpass the state-of-the-art for monocular depth estimation even with depth values for only 1 out of every \({\sim }10000\) image pixels, and we outperform other sparse-to-dense depth methods at all sparsity levels. With depth values for \(1{\slash }256\) of the image pixels, we achieve a mean error of less than \(1\%\) of actual depth on indoor scenes, comparable to the performance of consumer-grade depth sensor hardware. Our experiments demonstrate that it would indeed be possible to efficiently transform sparse depth measurements obtained using e.g. lower-power depth sensors or SLAM systems into high-quality dense depth maps.


Our DDPM of choice is trained on FFHQ #REFR dataset which contains 70, 000 unlabeled high-quality images.

3 , we plot the number of unique subjects that can be sampled as we increase the sample size.

The blue curve shows that the number of unique samples that can be generated by a DDPM of our choice does not saturate when we sample 200, 000 samples.

At 200, 000 samples, the unique subjects are about 60, 000.

And by extrapolating the curve, we estimate the number might reach 80, 000 with more samples.

The orange line shows the number of unique samples that are sufficiently different from the subjects in the CASIA-WebFace dataset.

The green line shows the number of unique samples left after filtering images that contain sunglasses.

The flat region is due to the filtering stage reducing the total candidates.

The plot shows that DDPM trained on FFHQ dataset can sufficiently generate a large number of unique and new samples that are different from CASIA-WebFace dataset.

However, with more samples, eventually there is a limit to the number of unique samples that can be generated.

Dcface: Synthetic Face Generation With Dual Condition Diffusion Model

Abstract: Generating synthetic datasets for training face recognition models is challenging because dataset generation entails more than creating high fidelity images. It involves generating multiple images of same subjects under different factors (e.g., variations in pose, illumination, expression, aging and occlusion) which follows the real image conditional distribution. Previous works have studied the generation of synthetic datasets using GAN or 3D models. In this work, we approach the problem from the aspect of combining subject appearance (ID) and external factor (style) conditions. These two conditions provide a direct way to control the inter-class and intra-class variations. To this end, we propose a Dual Condition Face Generator (DCFace) based on a diffusion model. Our novel Patch-wise style extractor and Time-step dependent ID loss enables DCFace to consistently produce face images of the same subject under different styles with precise control. Face recognition models trained on synthetic images from the proposed DCFace provide higher verification accuracies compared to previous works by 6.11% on average in 4 out of 5 test datasets, LFW, CFP-FP, CPLFW, AgeDB and CALFW. Code Link

A Style-Based Generator Architecture For Generative Adversarial Networks

Abstract: We propose an alternative generator architecture for generative adversarial networks, borrowing from style transfer literature. The new architecture leads to an automatically learned, unsupervised separation of high-level attributes (e.g., pose and identity when trained on human faces) and stochastic variation in the generated images (e.g., freckles, hair), and it enables intuitive, scale-specific control of the synthesis. The new generator improves the state-of-the-art in terms of traditional distribution quality metrics, leads to demonstrably better interpolation properties, and also better disentangles the latent factors of variation. To quantify interpolation quality and disentanglement, we propose two new, automated methods that are applicable to any generator architecture. Finally, we introduce a new, highly varied and high-quality dataset of human faces.


The Transformer model #REFR has been widely adopted and used in various research areas and industrial applications.

The most important issue of the model is quadratic complexity of attention operation, that makes large models increasingly difficult to apply to longer inputs. arXiv:2304.11062v1 [cs.CL]

Scaling Transformer To 1M Tokens And Beyond With Rmt

Abstract: Figure 1: Recurrent Memory Transformer retains information across up to 2×10 6 tokens. By augmenting a pre-trained BERT model with recurrent memory (Bulatov et al., 2022), we enabled it to store task-specific information across 7 segments of 512 tokens each. During inference, the model effectively utilized memory for up to 4,096 segments with a total length of 2,048,000 tokens-significantly exceeding the largest input size reported for transformer models (64K tokens for CoLT5 (Ainslie et al., 2023), and 32K tokens for GPT-4 (OpenAI, 2023)). This augmentation maintains the base model's memory size at 3.6 GB in our experiments.

Attention Is All You Need

Abstract: The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data.


A.4.1 Discussion On Comparison Among Generation-Based Methods

However, there is a phenomenon that is observed in other VAE-based generative approaches #REFR : while the latent vector is searched under a guidance to maximize desired properties, its decoded instance does not always have the properties or are not even valid.

Both MCTS and BP-VAE hardly generate any qualified TCRs (very low q%: 0.01±0.03% for MCTS and 0.04±0.09% for BP-VAEin P McPAS ).

MCTS explores potentially the entire sequence space, including both valid TCRs and non-TCRs.

Although it uses R to guide the search, with substantially more calls to calculate R than other methods, due to the fact that valid TCRs may only occupy an extreme small portion of the entire sequence space, it is extremely challenging for MCTS to find qualified TCRs.

In P VDJDB , MCTS even cannot find any qualified TCR for all the peptides within 1,000 rollouts, and thus has zero values at s v (C q ) and s r (C q ).

An estimation using TCRdb over all possible length-15 sequence space results at most This vector could correspond to a binding TCR and then is decoded to a TCR sequence.

This phenomenon also appears in BP-VAE: the decoded TCRs are not qualified most of the times.

This might be due to the propagation or magnification of the errors from the predictor in the latent space, or the exploration in the latent space ends at a region far away from that of valid TCRs.

However, theoretical justification behind VAE-based generative approaches is out of the scope of this paper.

T-Cell Receptor Optimization With Reinforcement Learning And Mutation Polices For Precision Immunotherapy

Abstract: T cells monitor the health status of cells by identifying foreign peptides displayed on their surface. T-cell receptors (TCRs), which are protein complexes found on the surface of T cells, are able to bind to these peptides. This process is known as TCR recognition and constitutes a key step for immune response. Optimizing TCR sequences for TCR recognition represents a fundamental step towards the development of personalized treatments to trigger immune responses killing cancerous or virus-infected cells. In this paper, we formulated the search for these optimized TCRs as a reinforcement learning (RL) problem, and presented a framework TCRPPO with a mutation policy using proximal policy optimization. TCRPPO mutates TCRs into effective ones that can recognize given peptides. TCRPPO leverages a reward function that combines the likelihoods of mutated sequences being valid TCRs measured by a new scoring function based on deep autoencoders, with the probabilities of mutated sequences recognizing peptides from a peptide-TCR interaction predictor. We compared TCRPPO with multiple baseline methods and demonstrated that TCRPPO significantly outperforms all the baseline methods to generate positive binding and valid TCRs. These results demonstrate the potential of TCRPPO for both precision immunotherapy and peptide-recognizing TCR motif discovery.

Automatic Chemical Design Using A Data-Driven Continuous Representation Of Molecules

Abstract: We report a method to convert discrete representations of molecules to and from a multidimensional continuous representation. This generative model allows efficient search and optimization through open-ended spaces of chemical compounds. We train deep neural networks on hundreds of thousands of existing chemical structures to construct two coupled functions: an encoder and a decoder. The encoder converts the discrete representation of a molecule into a real-valued continuous vector, and the decoder converts these continuous vectors back to the discrete representation from this latent space. Continuous representations allow us to automatically generate novel chemical structures by performing simple operations in the latent space, such as decoding random vectors, perturbing known chemical structures, or interpolating between molecules. Continuous representations also allow the use of powerful gradient-based optimization to efficiently guide the search for optimized functional compounds. We demonstrate our method in the design of drug-like molecules as well as organic light-emitting diodes.


After matching, like DeepSORT #REFR , we need to handle the born and death of tracklets.

Due to the continuous relaxation, the output of the QP layer may not be binary.

To get a valid assignment, we use the greedy rounding strategy to generate the final permutation matrix from the predicted matching score map, i.e., we match the detection with the tracklet with the maximum score.

We keep the matching between detection and tracklet only if it satisfies all the following constraints: 1) The appearance similarity between detection and tracklet is above the threshold σ.

2) The detection is not far away from the tracklet.

We set a threshold κ as the Mahalanobis distance between the predicted distribution of the tracklet bounding box by the motion model and the detection bounding box in pixel coordinates, called the motion gate.

3) The detection bounding box overlaps with the position of tracklet predicted by the motion model. The constraints above can be written as

Learnable Graph Matching: A Practical Paradigm For Data Association

Abstract: Data association is at the core of many computer vision tasks, e.g., multiple object tracking, image matching, and point cloud registration. Existing methods usually solve the data association problem by network flow optimization, bipartite matching, or end-to-end learning directly. Despite their popularity, we find some defects of the current solutions: they mostly ignore the intra-view context information; besides, they either train deep association models in an end-to-end way and hardly utilize the advantage of optimization-based assignment methods, or only use an off-the-shelf neural network to extract features. In this paper, we propose a general learnable graph matching method to address these issues. Especially, we model the intra-view relationships as an undirected graph. Then data association turns into a general graph matching problem between graphs. Furthermore, to make optimization end-to-end differentiable, we relax the original graph matching problem into continuous quadratic programming and then incorporate training into a deep graph neural network with KKT conditions and implicit function theorem. In MOT task, our method achieves state-of-the-art performance on several MOT datasets. For image matching, our method outperforms state-of-the-art methods with half training data and iterations on a popular indoor dataset, ScanNet. Code will be available at https://github.com/jiaweihe1996/GMTracker.

Simple Online And Realtime Tracking With A Deep Association Metric

Abstract: Simple Online and Realtime Tracking (SORT) is a pragmatic approach to multiple object tracking with a focus on simple, effective algorithms. In this paper, we integrate appearance information to improve the performance of SORT. Due to this extension we are able to track objects through longer periods of occlusions, effectively reducing the number of identity switches. In spirit of the original framework we place much of the computational complexity into an offline pre-training stage where we learn a deep association metric on a large-scale person re-identification dataset. During online application, we establish measurement-to-track associations using nearest neighbor queries in visual appearance space. Experimental evaluation shows that our extensions reduce the number of identity switches by 45%, achieving overall competitive performance at high frame rates.


We remark that our comparison does not include the spiking predictive coding model reported in #REFR , even though a slightly better generalization error was reported on MNIST.

In Table 1 , we present our simulation results for our recurrent spiking model trained with event-driven forwardforward learning.

Notice that, like many bio-physical spiking networks, even though we do not quite match the performance of backprop-based feedforward networks (essentially a purely rate-coded system), our generalization error comes surprisingly close.

This is particularly promising given that the predicted context layer is a layer of spiking neurons itself.

Furthermore, our ED-FF model comes the closest to matching the BP-FNN rate-coded baseline compared to the other SNN credit assignment algorithms.

The BFA SNN comes in a close second place, illustrating that feedback synapses are still quite a powerful mechanism even though our goal was to demonstrate effective learning without feedback.

This model was not included given that is unsupervised and required post-fitting a log-linear classifier to rate code approximations of its top-most spike-train representations (and our focus in this study was on spiking models that jointly learned a spiking classifier with the internal representations of the sensory input).

In Figure 2 , we examine the clusters that emerge within the latent space induced by a recurrent spiking network trained with ED-FF.

To compute the latent vectors/codes, we form an approximate rate code from each data point's resultant top-layer (layer s 3 t ) spike trains as follows:

Learning Spiking Neural Systems With The Event-Driven Forward-Forward Process

Abstract: We develop a novel credit assignment algorithm for information processing with spiking neurons without requiring feedback synapses. Specifically, we propose an event-driven generalization of the forward-forward and the predictive forward-forward learning processes for a spiking neural system that iteratively processes sensory input over a stimulus window. As a result, the recurrent circuit computes the membrane potential of each neuron in each layer as a function of local bottom-up, top-down, and lateral signals, facilitating a dynamic, layer-wise parallel form of neural computation. Unlike spiking neural coding, which relies on feedback synapses to adjust neural electrical activity, our model operates purely online and forward in time, offering a promising way to learn distributed representations of sensory data patterns with temporal spike signals. Notably, our experimental results on several pattern datasets demonstrate that the even-driven forward-forward (ED-FF) framework works well for training a dynamic recurrent spiking system capable of both classification and reconstruction.

Spiking Neural Predictive Coding For Continual Learning From Data Streams

Abstract: For energy-efficient computation in specialized neuromorphic hardware, we present the Spiking Neural Coding Network, an instantiation of a family of artificial neural models strongly motivated by the theory of predictive coding. The model, in essence, works by operating in a never-ending process of "guess-and-check", where neurons predict the activity values of one another and then immediately adjust their own activities to make better future predictions. The interactive, iterative nature of our neural system fits well into the continuous time formulation of data sensory stream prediction and, as we show, the model's structure yields a simple, local synaptic update rule, which could be used to complement or replace online spike-timing dependent plasticity rules. In this article, we experiment with an instantiation of our model that consists of leaky integrate-and-fire units. However, the general framework within which our model is situated can naturally incorporate more complex, formal neurons such as the Hodgkin-Huxley model. Our experimental results in pattern recognition demonstrate the potential of the proposed model when binary spike trains are the primary paradigm for inter-neuron communication. Notably, our model is competitive in terms of classification performance, capable of conducting online semi-supervised learning, and more computationally economical and biologically-plausible than popular artificial neural networks.


GCNs obtain convolution-like operations by aggregating information about the neighbors of a node using methods from spectral graph theory and using nonlinear activation functions to obtain low-dimensional features of graph nodes #REFR .

Data in these domains are different from traditional Euclidean data in that it has an asymmetric and irregular structure.

And describing these data by graphs can well express the structural characteristics of these data and show powerful representation.

Meanwhile, graph analysis methods in machine learning can make good use of such non-Euclidean structured data and be used for tasks such as node classification, link prediction, and clustering.

Graph neural networks (GNNs), a widely used graph analysis method, process graph-structured data by performing deep learning operations on the graph domain and have made remarkable progress due to their excellent performance.

While in Graph Neural Networks (GNNs), Convolutional Neural Networks (CNNs) are extended to graph-structured data by introducing Graph Convolutional Networks (GCNs).

Then, GCNs requires the entire graph to be loaded into memory for convolution during training, which is inefficient for large graph transformation work patterns.

GraphSAGE #OTHEREFR is an inductive graph convolution method that uses sampling of some of the nodes for learning and aggregation functions to learn feature information aggregated from the neighborhoods of the nodes.

It is GraphSAGE learning by sampling that makes the convolution operation does not need to load the complete graph, which is a significant improvement over GCNs in the processing of large graphs.

Many different improved versions have been proposed by researchers since then #OTHEREFR , and all have achieved promising results.

GCNs are mainly built on a semi-supervised paradigm #OTHEREFR , where the Laplacian matrix of the graph is utilized for convolutional operations, and message aggregation is constrained by the adjacency matrix of the graph.

Hd-Gcn:A Hybrid Diffusion Graph Convolutional Network

Abstract: In tasks involving graph-structured data, Graph Convolutional Networks (GCNs) have brought promising results by introducing convolution into Graph Neural Networks (GNNs). However, the information diffusion performance of GCNs and its variant models is limited by the adjacency matrix, which can lower their performance. Therefore, we introduce a new framework for graph convolutional networks called the Hybrid Diffusion Graph Convolutional Network (HD-GCN) to address the limitations of information diffusion caused by the adjacency matrix. In the HD-GCN framework, we initially utilize diffusion maps to facilitate the diffusion of information among nodes that are adjacent to each other in the feature space. This allows for the diffusion of information between similar points that may not have an adjacent relationship. Next, we utilize graph convolution to further propagate information among adjacent nodes after the diffusion maps, thereby enabling the spread of information among similar nodes that are adjacent in the graph. Finally, we employ the diffusion distances obtained through the use of diffusion maps to regularize and constrain the predicted labels of training nodes. This regularization method is then applied to the HD-GCN training, resulting in a smoother classification surface. The model proposed in this paper effectively overcomes the limitations of information diffusion imposed only by the adjacency matrix. HD-GCN utilizes hybrid diffusion by combining information diffusion between neighborhood nodes in the feature space and adjacent nodes in the adjacency matrix. This method allows for more comprehensive information propagation among nodes, resulting in improved model performance. We evaluated the performance of HD-GCN on three well-known citation network datasets and the results showed that the proposed framework is more effective than several graph-based semi-supervised learning methods.

Semi-Supervised Classification With Graph Convolutional Networks

Abstract: We present a scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs. We motivate the choice of our convolutional architecture via a localized first-order approximation of spectral graph convolutions. Our model scales linearly in the number of graph edges and learns hidden layer representations that encode both local graph structure and features of nodes. In a number of experiments on citation networks and on a knowledge graph dataset we demonstrate that our approach outperforms related methods by a significant margin.


Most visualization software aims for the accurate reproduction of data using simple, abstract 2-dimensional graphical marks: the rectangles (◻) of the bar chart; the arcs (⪦) of the pie; and the mix of circles (◯), rectangles (◻), and occasional stars ( ) in scatter plots #REFR .

In many situations, these representations are insufficient for a creative design vision. Graphical 'embellishments' require additional styling and design work.

A designer might choose to stylize a visualization by replacing simple abstract marks with alternative representations: candles instead of bars, slices of fruit instead of arcs in a pie chart, and flowers instead of circles in a scatter plot.

Beyond the simple replacement of marks, visualization styling can use alternative artistic forms and media (a sketch, a painting, a photograph, clay, etc.).

Our particular focus is on stylized forms that represent a significant stylistic change to how the mark is represented (e.g., stacked coffee cups), but are still recognizable in their original idiomatic forms (e.g., a bar chart) #OTHEREFR .

Due to the uniqueness of many of these representations, for a designer to achieve their vision, they need additional tools, skills, and procedures.

Viz2Viz: Prompt-Driven Stylized Visualization Generation Using A Diffusion Model

Abstract: Fig. 1: viz2viz generated samples (original data inset). From left to right: bird eye view of green forests with many trees, blue ocean with ships, grey city with many buildings, orange deserts; realistic stacks of red coca-cola coke cans, brown tea cups, glass wine bottles, starbucks paper coffee cups; realistic pink tulips; Ukiyo-e style side view of red wooden roller coaster, Ukiyo-e style blue sea waves and surfers. Note that in some examples in this paper, we select prompts to demonstrate the capabilities of viz2viz rather than for their aesthetic or design qualities.

Critical Reflections On Visualization Authoring Systems

Abstract: An emerging generation of visualization authoring systems support expressive information visualization without textual programming. As they vary in their visualization models, system architectures, and user interfaces, it is challenging to directly compare these systems using traditional evaluative methods. Recognizing the value of contextualizing our decisions in the broader design space, we present critical reflections on three systems we developed —Lyra, Data Illustrator, and Charticulator. This paper surfaces knowledge that would have been daunting within the constituent papers of these three systems. We compare and contrast their (previously unmentioned) limitations and trade-offs between expressivity and learnability. We also reflect on common assumptions that we made during the development of our systems, thereby informing future research directions in visualization authoring systems.


One way to circumvent these difficulties is to embed pooling layers along the CLs #REFR .

In addition, given a deep architecture and the ratios between the depths of consecutive CLs, SRs increase as a function of the first CL depth #OTHEREFR .

The deep learning strategy resulted in several practical difficulties, including the following.

First, although the depth increases along the deep architecture, the input size of the layers remains fixed.

The second difficulty is that the last CL output size, depth × layer input size, becomes very large, serving as the first FC layer input, which consists of a large number of tunable parameters.

These computational complexities overload even powerful GPUs, limited by the accelerated utilization of a large number of filters and sizes of the FC layers.

Each pooling reduces the output dimension of a CL by combining a cluster of outputs, e.g., 2 × 2, at one, and such operations along the deep architecture reduce the CL dimension by a factor 4 .

The most popular pooling operators are max-pooling (MP) #OTHEREFR , which implements the maximal value of each cluster, and average pooling (AP) #OTHEREFR , which implements the average value of each cluster; however, more types of pooling operators exist #OTHEREFR .

The core question in this work is whether SRs can be enhanced depending on the location of the pooling operators along the CLs of a given deep architecture.

For instance, VGG16 consists of 13 CLs, three FC layers, and five (2 × 2) MP operators located along the CLs 2 (Fig. 1A) .

The results indicate that SRs can be significantly increased by a smaller number of pooling operators adjacent to the last CL with optionally larger pooling sizes, for example, the advanced VGG16 (Fig. 1B) .

Enhancing The Success Rates By Performing Pooling Decisions Adjacent To The Output Layer

Abstract: Learning classification tasks of (2 × 2) inputs typically consist of ≤ (2 × 2) max-pooling (MP) operators along the entire feedforward deep architecture. Here we show, using the CIFAR-10 database, that pooling decisions adjacent to the last convolutional layer significantly enhance accuracy success rates (SRs). In particular, average SRs of the advanced-VGG with layers (A-VGGm) architectures are 0.936, 0.940, 0.954, 0.955, and 0.955 for m=6, 8, 14, 13, and 16, respectively. The results indicate A-VGG8's SR is superior to VGG16's, and that the SRs of A-VGG13 and A-VGG16 are equal, and comparable to that of Wide-ResNet16. In addition, replacing the three fully connected (FC) layers with one FC layer, A-VGG6 and A-VGG14, or with several linear activation FC layers, yielded similar SRs. These significantly enhanced SRs stem from training the most influential input-output routes, in comparison to the inferior routes selected following multiple MP decisions along the deep architecture. In addition, SRs are sensitive to the order of the non-commutative MP and average pooling operators adjacent to the output layer, varying the number and location of training routes. The results call for the reexamination of previously proposed deep architectures and their SRs by utilizing the proposed pooling strategy adjacent to the output layer.

Very Deep Convolutional Networks For Large-Scale Image Recognition

Abstract: In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3x3) convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respectively. We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results. We have made our two best-performing ConvNet models publicly available to facilitate further research on the use of deep visual representations in computer vision.


The Baseline employs the T2I attention from SCAN #REFR and averages all the cosine similarities as the final score. 1) Correspondence regulator. Eq.

In this section, we first report the configurations of our proposed regulators, as well as the initialization and optimization of the attention factors.

Then, we delve into the RAR and RCR to display how the aggregation weights and cross-attention distributions are progressively refined. Finally, we also explore alternative strategies and architectures.

All comparisons are implemented based on SCAN #OTHEREFR unless otherwise noted.

In TABLE III, we carry out critical analyses of the influence of residual architectures.

#OTHEREFR indicates that the current adaptive weight vector e at the last step.

Here, we remove these two variables to construct a no-residual version of the RCR.

Compared with the residual structure, the RCR without residual design results in an obvious R@1 drop in TABLE III, indicating that RCR is inclined to predict offsets against the current state to adjust previous regulation dynamically.

To be specific, 1-step RCR without residual fashion produces better results than Baseline.

This is because in the beginning, each word shares the same initialization of a weight vector e (0) =1 d and temperature λ (0) =10, and the RCR barely infers the absolute value of these attention factors in the next step.

Plug-And-Play Regulators For Image-Text Matching

Abstract: Exploiting fine-grained correspondence and visualsemantic alignments has shown great potential in image-text matching. Generally, recent approaches first employ a crossmodal attention unit to capture latent region-word interactions, and then integrate all the alignments to obtain the final similarity. However, most of them adopt one-time forward association or aggregation strategies with complex architectures or additional information, while ignoring the regulation ability of network feedback. In this paper, we develop two simple but quite effective regulators which efficiently encode the message output to automatically contextualize and aggregate cross-modal representations. Specifically, we propose (i) a Recurrent Correspondence Regulator (RCR) which facilitates the cross-modal attention unit progressively with adaptive attention factors to capture more flexible correspondence, and (ii) a Recurrent Aggregation Regulator (RAR) which adjusts the aggregation weights repeatedly to increasingly emphasize important alignments and dilute unimportant ones. Besides, it is interesting that RCR and RAR are "plug-and-play": both of them can be incorporated into many frameworks based on cross-modal interaction to obtain significant benefits, and their cooperation achieves further improvements. Extensive experiments on MSCOCO and Flickr30K datasets validate that they can bring an impressive and consistent R@1 gain on multiple models, confirming the general effectiveness and generalization ability of the proposed methods.

Stacked Cross Attention For Image-Text Matching

Abstract: In this paper, we study the problem of image-text matching. Inferring the latent semantic alignment between objects or other salient stuff (e.g. snow, sky, lawn) and the corresponding words in sentences allows to capture fine-grained interplay between vision and language, and makes image-text matching more interpretable. Prior work either simply aggregates the similarity of all possible pairs of regions and words without attending differentially to more and less important words or regions, or uses a multi-step attentional process to capture limited number of semantic alignments which is less interpretable. In this paper, we present Stacked Cross Attention to discover the full latent alignments using both image regions and words in a sentence as context and infer image-text similarity. Our approach achieves the state-of-the-art results on the MS-COCO and Flickr30K datasets. On Flickr30K, our approach outperforms the current best methods by 22.1% relatively in text retrieval from image query, and 18.2% relatively in image retrieval with text query (based on Recall@1). On MS-COCO, our approach improves sentence retrieval by 17.8% relatively and image retrieval by 16.6% relatively (based on Recall@1 using the 5K test set). Code has been made available at: https://github.com/kuanghuei/SCAN.


Federated Learning (FL) is a Machine Learning (ML) technique that has gained tremendous popularity in the last years #REFR .

Its core idea is to orchestrate the training of a global ML model without ever exchanging the data owned by each party or requiring it to be gathered in one common computational infrastructure.

The popularity of FL caused the development of a plethora of FL frameworks, e.g., Intel ® OpenFL #OTHEREFR , Flower #OTHEREFR , TensorFlow Federated [1] , and HPE Swarm Learning #OTHEREFR to cite a few.

This software only supports one ML model type: Deep Neural Networks (DNNs).

While DNNs have shown unprecedented results across a wide range of applications, from image recognition #OTHEREFR to natural language processing #OTHEREFR , from drug discovery #OTHEREFR to fraud detection #OTHEREFR , they are not the best ML model for every use case.

First, DNNs require massive amounts of data, and collecting and labelling enough high-quality samples is often prohibitive.

Model-Agnostic Federated Learning

Abstract: Since its debut in 2016, Federated Learning (FL) has been tied to the inner workings of Deep Neural Networks (DNNs). On the one hand, this allowed its development and widespread use as DNNs proliferated. On the other hand, it neglected all those scenarios in which using DNNs is not possible or advantageous. The fact that most current FL frameworks only allow training DNNs reinforces this problem. To address the lack of FL solutions for non-DNN-based use cases, we propose MAFL (Model-Agnostic Federated Learning). MAFL marries a modelagnostic FL algorithm, AdaBoost.F, with an open industry-grade FL framework: Intel ® OpenFL. MAFL is the first FL system not tied to any specific type of machine learning model, allowing exploration of FL scenarios beyond DNNs and trees. We test MAFL from multiple points of view, assessing its correctness, flexibility and scaling properties up to 64 nodes. We optimised the base software achieving a 5.5x speedup on a standard FL scenario. MAFL is compatible with x86-64, ARM-v8, Power and RISC-V.

Advances And Open Problems In Federated Learning

Abstract: Federated learning (FL) is a machine learning setting where many clients (e.g. mobile devices or whole organizations) collaboratively train a model under the orchestration of a central server (e.g. service provider), while keeping the training data decentralized. FL embodies the principles of focused data collection and minimization, and can mitigate many of the systemic privacy risks and costs resulting from traditional, centralized machine learning and data science approaches. Motivated by the explosive growth in FL research, this paper discusses recent advances and presents an extensive collection of open problems and challenges.


This estimator can be massively parallelized (we denote the number of such iid estimators by N ) #REFR ; however, it is not online and might incur large latency between gradient updates when T is large.

With the score function gradient estimator trick #OTHEREFR , an unbiased estimator of (2) is given by 1 σ 2 L([θ + ] ×T ) .

This estimator is zeroth-order since it only requires the loss function evaluation but not an explicit computation of its gradient, which allows for its effective use in cases when the gradients are noninformative (due to chaos) or not directly computable (black-box/discontinuous loss).

To reduce the variance of this estimator, antithetic sampling is used and we call this finite-difference-style estimator FullES:

Here the term Full highlights that this estimator can only produce a gradient estimate by averaging the antithetic particles' losses (see Equation (1)) after a full unroll of T steps.

θ Worker 1 1 N N ∑ i=1 g i ⏟ → s τ N +1 → ↑ θ ↑ … → s τ N +W ↑ ↑ L τ N +1 L τ N +W s τ N g N g 1 Horizon length T Truncation window length W +ϵ θ +ϵ Worker N Online Evolution Strategies Protocol (a) PESWorker ϵ 1 ϵ 2 ϵ 3 Accumulated noise ξ ϵ 1 GPES Worker K=2W ϵ 1 ϵ 2 ϵ 1 ϵ 1 Reuse ϵ 1 ϵ 1 Sample

Figure 2: (a) Illustration of step-unlocked Online ES workers working independently at different truncation windows.

Here a central server sends θ (whose gradient to be estimated) to each worker and receives the estimates over partial unrolls from each.

The averaged gradient is then used in a first-order optimization algorithm (like Adam).

Noise-Reuse In Online Evolution Strategies

Abstract: Online evolution strategies have become an attractive alternative to automatic differentiation (AD) due to their ability to handle chaotic and black-box loss functions, while also allowing more frequent gradient updates than vanilla Evolution Strategies (ES). In this work, we propose a general class of unbiased online evolution strategies. We analytically and empirically characterize the variance of this class of gradient estimators and identify the one with the least variance, which we term Noise-Reuse Evolution Strategies (NRES). Experimentally, we show that NRES results in faster convergence than existing AD and ES methods in terms of wall-clock speed and total number of unroll steps across a variety of applications, including learning dynamical systems, meta-training learned optimizers, and reinforcement learning.

Evolution Strategies As A Scalable Alternative To Reinforcement Learning

Abstract: We explore the use of Evolution Strategies (ES), a class of black box optimization algorithms, as an alternative to popular MDP-based RL techniques such as Q-learning and Policy Gradients. Experiments on MuJoCo and Atari show that ES is a viable solution strategy that scales extremely well with the number of CPUs available: By using a novel communication strategy based on common random numbers, our ES implementation only needs to communicate scalars, making it possible to scale to over a thousand parallel workers. This allows us to solve 3D humanoid walking in 10 minutes and obtain competitive results on most Atari games after one hour of training. In addition, we highlight several advantages of ES as a black box optimization technique: it is invariant to action frequency and delayed rewards, tolerant of extremely long horizons, and does not need temporal discounting or value function approximation.


HAL #REFR was used to perform fundamental operations on the netlist such as graph traversal and storing and retrieving data on design elements.

An RTL description is written for all the identified modules.

They are instantiated in the top-level module to form a complete netlist.

The obtained high-level RTL is then verified using JasperGold for equivalence checking with the source HDL to confirm functional validity.

However, to ensure equivalence in sequential circuits, we reset all flip-flops in the source HDL that do not have an initialization.

The techniques described in this paper were implemented using Python.

All the benchmark designs were first synthesized using Artix 7-series and Zync 7series FPGAs using Xilinx Vivado.

We set the flatten hierarchy option to full, while we kept the other default synthesis and optimization settings.

We delete all the original net IDs that may indicate the signal name in the original model and generate unique random IDs for all nets. We use these netlists for our reverse engineering experiments. Yosys #OTHEREFR was used for the QBF SAT problem.

Permutation-independent Boolean matching was performed using testnpn #OTHEREFR command in abc #OTHEREFR .

All experiments were done on an AMD Ryzen 7 processor with 16GB RAM.

Reverse Engineering Word-Level Models From Look-Up Table Netlists

Abstract: Reverse engineering of FPGA designs from bitstreams to RTL models aids in understanding the high level functionality of the design and for validating and reconstructing legacy designs. Fast carry-chains are commonly used in synthesis of operators in FPGA designs. We propose a method to detect word-level structures by analyzing these carry-chains in LUT (Look-Up Table) level netlists. We also present methods to adapt existing techniques to identify combinational operations and sequential modules in ASIC netlists to LUT netlists. All developed and adapted techniques are consolidated into an integrated tool-chain to aid in reverse engineering of word-level designs from LUT-level netlists. When evaluated on a set of real-world designs, the tool-chain infers 34% to 100% of the elements in the netlist to be part of a known word-level operation or a known sequential module.

Highway To Hal: Open-Sourcing The First Extendable Gate-Level Netlist Reverse Engineering Framework

Abstract: Since hardware oftentimes serves as the root of trust in our modern interconnected world, malicious hardware manipulations constitute a ubiquitous threat in the context of the Internet of Things (IoT). Hardware reverse engineering is a prevalent technique to detect such manipulations. Over the last years, an active research community has significantly advanced the field of hardware reverse engineering. Notably, many open research questions regarding the extraction of functionally correct netlists from Field Programmable Gate Arrays (FPGAs) or Application Specific Integrated Circuits (ASICs) have been tackled. In order to facilitate further analysis of recovered netlists, a software framework is required, serving as the foundation for specialized algorithms. Currently, no such framework is publicly available. Therefore, we provide the first open-source gate-library agnostic framework for gate-level netlist analysis. In this positional paper, we demonstrate the workflow of our modular framework HAL on the basis of two case studies and provide profound insights on its technical foundations.


Question-Driven Claim Extraction With Validation

To this end, we employ an answer sentence selection model #REFR that validates each of the extracted contexts (from Section 4.3.1) separately against the strategic question.

Following #OTHEREFR , we adopt a Question Answering (QA) formulation to identify claims relevant to a given strategic question.

Specifically, we design a QA pipeline, utilizing a transformer-based RoBERTa-large encoder model #OTHEREFR variant 6 that has been trained on SQuAD 2.0 #OTHEREFR and Natural Questions #OTHEREFR .

The pipeline takes as input the news corpus split into snippets along with the strategic question, and outputs short answer extractions to these questions.

The identified short answers are then expanded, by including the 3-sentence window around it to provide additional context.

However, there is still a risk of false positives #OTHEREFR being identified as candidate answers with high confidence, thus necessitating the validation #OTHEREFR of extracted answers.

We concatenate the question and extracted context as input to a binary classification model 7 , with an underlying RoBERTa-large backbone, that is trained on Natural Questions #OTHEREFR and WikiQA #OTHEREFR .

The output of the model is a validation score, between 0 (incorrect answer selection) and 1 (correct answer selection), used to select the top-5 relevant contexts for summarization. Fig.

3: Figure showing an example for how multimodal information (in the form of images) supports and provides additional context to the claims presented in SmartBook.

In this example, the presence of anti-aircraft weapons (as seen in the image) in Ukraine provides background for the discussion in NATO on whether to impose a no-fly zone.

Situation reports often rely on a variety of sources, including text, images, videos, and audio recordings, to provide a holistic view of events.

Smartbook: Ai-Assisted Situation Report Generation

Abstract: Emerging events, such as the COVID pandemic and the Ukraine Crisis, require a time-sensitive comprehensive understanding of the situation to allow for appropriate decision-making and effective action response. Automated generation of situation reports can significantly reduce the time, effort, and cost for domain experts when preparing their official human-curated reports. However, AI research toward this goal has been very limited, and no successful trials have yet been conducted to automate such report generation. Pre-existing natural language processing methods, large language model based text generation, and information retrieval techniques are insufficient to identify, locate, and summarize important information, and lack detailed, structured, and strategic awareness. We propose SmartBook, a novel task formulation targeting situation report generation, which consumes large volumes of news data to produce a structured situation report with multiple hypotheses (claims) summarized and grounded with rich links to factual evidence. We realize SmartBook for the Ukraine-Russia crisis by automatically generating intelligence analysis reports to assist expert analysts. The machine-generated reports are structured in the form of timelines, with each timeline organized by major events (or chapters), corresponding strategic questions (or sections) and their grounded summaries (or section content). Our proposed framework automatically detects realtime event-related strategic questions, which are more directed than manually-crafted analyst questions, which tend to be too complex, hard

Tanda: Transfer And Adapt Pre-Trained Transformer Models For Answer Sentence Selection

Abstract: We propose TANDA, an effective technique for fine-tuning pre-trained Transformer models for natural language tasks. Specifically, we first transfer a pre-trained model into a model for a general task by fine-tuning it with a large and high-quality dataset. We then perform a second fine-tuning step to adapt the transferred model to the target domain. We demonstrate the benefits of our approach for answer sentence selection, which is a well-known inference task in Question Answering. We built a large scale dataset to enable the transfer step, exploiting the Natural Questions dataset. Our approach establishes the state of the art on two well-known benchmarks, WikiQA and TREC-QA, achieving MAP scores of 92% and 94.3%, respectively, which largely outperform the previous highest scores of 83.4% and 87.5%, obtained in very recent work. We empirically show that TANDA generates more stable and robust models reducing the effort required for selecting optimal hyper-parameters. Additionally, we show that the transfer step of TANDA makes the adaptation step more robust to noise. This enables a more effective use of noisy datasets for fine-tuning. Finally, we also confirm the positive impact of TANDA in an industrial setting, using domain specific datasets subject to different types of noise.


We follow the two-step scheme (as detailed in § 4) and the training settings in #REFR .

Specifically, for each step, the model is trained for 640k training iterations with batch size 512.

We adopt the Adam optimizer #OTHEREFR and set the initial learning rate to 10 −3 .

Weight decay rates in the first and second steps are 10 −5 and 0, respectively.

For experiments on ImageNet, models are trained with 8 V100 GPUs.

We follow the training settings and data augmentation strategies in #OTHEREFR .

Compacting Binary Neural Networks By Sparse Kernel Selection

Abstract: Binary Neural Network (BNN) represents convolution weights with 1-bit values, which enhances the efficiency of storage and computation. This paper is motivated by a previously revealed phenomenon that the binary kernels in successful BNNs are nearly power-law distributed: their values are mostly clustered into a small number of codewords. This phenomenon encourages us to compact typical BNNs and obtain further close performance through learning nonrepetitive kernels within a binary kernel subspace. Specifically, we regard the binarization process as kernel grouping in terms of a binary codebook, and our task lies in learning to select a smaller subset of codewords from the full codebook. We then leverage the Gumbel-Sinkhorn technique to approximate the codeword selection process, and develop the Permutation Straight-Through Estimator (PSTE) that is able to not only optimize the selection process end-toend but also maintain the non-repetitive occupancy of selected codewords. Experiments verify that our method reduces both the model size and bit-wise computational costs, and achieves accuracy improvements compared with stateof-the-art BNNs under comparable budgets.

Reactnet: Towards Precise Binary Neural Network With Generalized Activation Functions

Abstract: In this paper, we propose several ideas for enhancing a binary network to close its accuracy gap from real-valued networks without incurring any additional computational cost. We first construct a baseline network by modifying and binarizing a compact real-valued network with parameter-free shortcuts, bypassing all the intermediate convolutional layers including the downsampling layers. This baseline network strikes a good trade-off between accuracy and efficiency, achieving superior performance than most of existing binary networks at approximately half of the computational cost. Through extensive experiments and analysis, we observed that the performance of binary networks is sensitive to activation distribution variations. Based on this important observation, we propose to generalize the traditional Sign and PReLU functions, denoted as RSign and RPReLU for the respective generalized functions, to enable explicit learning of the distribution reshape and shift at near-zero extra cost. Lastly, we adopt a distributional loss to further enforce the binary network to learn similar output distributions as those of a real-valued network. We show that after incorporating all these ideas, the proposed ReActNet outperforms all the state-of-the-arts by a large margin. Specifically, it outperforms Real-to-Binary Net and MeliusNet29 by 4.0% and 3.6% respectively for the top-1 accuracy and also reduces the gap to its real-valued counterpart to within 3.0% top-1 accuracy on ImageNet dataset.


In Section 7 we include a setting for abstract wave equations, which was considered in #REFR only for the splitting scheme.

For other schemes the contractivity of R usually follows by a functional calculus argument (see Proposition 2.4 below).

In the above, one usually takes Y to be a suitable intermediate space between X and D(A).

In the special and important case that Y = D(A) one can take α = #OTHEREFR 2 for all of the aforementioned schemes.

More general convergence rates can be found in Table 1 and Maxwell equations are included in the main text (see Subsections 3.3, 6.4, and 6.5).

Our results improve several results from the literature to more general schemes and general rates α.

We prove similar higher order convergence rates for more general schemes, and in particular recover #OTHEREFR as a special case.

To make the above results applicable to implementable numerical schemes for SPDEs, one would additionally need a space discretisation.

Since the main novelty of our work lies in the treatment of temporal discretisations, we will only consider the latter.

A detailed understanding of the global Lipschitz setting is a quintessential step towards the treatment of locally Lipschitz nonlinearities, which occur more frequently in practice.

Our result should be seen as a first step, and we plan to continue our work on uniform strong errors in a locally Lipschitz setting in the near future.

Pathwise Uniform Convergence Of Time Discretisation Schemes For Spdes

Abstract: In this paper we prove convergence rates for time discretisation schemes for semilinear stochastic evolution equations with additive or multiplicative Gaussian noise, where the leading operator A is the generator of a strongly continuous semigroup S on a Hilbert space X, and the focus is on non-parabolic problems. The main results are optimal bounds for the uniform strong error E ∞ k := E sup j∈{0,...,N k } U (t j) − U j p 1/p , where p ∈ [2, ∞), U is the mild solution, U j is obtained from a time discretisation scheme, k is the step size, and N k = T /k. The usual schemes such as splitting/exponential Euler, implicit Euler, and Crank-Nicolson, etc. are included as special cases. Under conditions on the nonlinearity and the noise we show • E ∞ k k log(T /k) (linear equation, additive noise, general S); • E ∞ k √ k log(T /k) (nonlinear equation, multiplicative noise, contractive S); • E ∞ k k log(T /k) (nonlinear wave equation, multiplicative noise). The logarithmic factor can be removed if the splitting scheme is used with a (quasi)-contractive S. The obtained bounds coincide with the optimal bounds for SDEs. Most of the existing literature is concerned with bounds for the simpler pointwise strong error E k := sup j∈{0,...,N k } E U (t j) − U j p 1/p. Applications to Maxwell equations, Schrödinger equations, and wave equations are included. For these equations our results improve and reprove several existing results with a unified method.

An Exponential Integrator Scheme For Time Discretization Of Nonlinear Stochastic Wave Equation

Abstract: This work is devoted to convergence analysis of an exponential integrator scheme for semi-discretization in time of nonlinear stochastic wave equation. A unified framework is first set forth, which covers important cases of additive and multiplicative noises. Within this framework, the proposed scheme is shown to converge uniformly in the strong $L^p$-sense with precise convergence rates given. The abstract results are then applied to several concrete examples. Further, weak convergence rates of the scheme are examined for the case of additive noise. To analyze the weak error for the nonlinear case, techniques based on the Malliavin calculus were usually exploited in the literature. Under certain appropriate assumptions on the nonlinearity, this paper provides a weak error analysis, which does not rely on the Malliavin calculus. The rates of weak convergence can, as expected, be improved in comparison with the strong rates. Both strong and weak convergence results obtained here show that the proposed method achieves higher convergence rates than the implicit Euler and Crank-Nicolson time discretizations. Numerical results are finally reported to confirm our theoretical findings.


Lower Bound For Inverse Trace Estimation

Note that this result is very similar to that of #REFR , except that we work with the inverse trace rather than the minimum eigenvalue.

, w n , the matrix W has the Wishart(d − n) distribution.

For any matrices Y 1 ∈ R n×n , Y 2 ∈ R (d−n)×n , and any symmetric matrix W ∈ R (d−n)×(d−n) , it holds that

λ min Y 1 Y 1 Y 1 Y 2 Y 2 Y 1 Y 2 Y 2 + W ≤ λ min ( W ) .

[Proof of Theorem 18] Let δ > 0 be chosen later.

We first argue that tr must not be too large.

Applying Proposition 24, we conclude that there is a universal constant C > 0 such that tr(W −1 ) ≤ C d 2 with probability at least 1/2. Hence,

P tr ≤ CC d 2 ≥ P tr(W −1 ) ≤ C d 2 and tr ≤ C tr(W −1 ) ≥ P{tr(W −1 ) ≤ C d 2 } − P{ tr > C tr(W −1 )} ≥ 1 2 − δ .

Query Lower Bounds For Log-Concave Sampling

Abstract: Log-concave sampling has witnessed remarkable algorithmic advances in recent years, but the corresponding problem of proving lower bounds for this task has remained elusive, with lower bounds previously known only in dimension one. In this work, we establish the following query lower bounds: (1) sampling from strongly log-concave and log-smooth distributions in dimension d ≥ 2 requires Ω(log κ) queries, which is sharp in any constant dimension, and (2) sampling from Gaussians in dimension d (hence also from general log-concave and log-smooth distributions in dimension d) requires Ω(min(√ κ log d, d)) queries, which is nearly sharp for the class of Gaussians. Here κ denotes the condition number of the target distribution. Our proofs rely upon (1) a multiscale construction inspired by work on the Kakeya conjecture in harmonic analysis, and (2) a novel reduction that demonstrates that block Krylov algorithms are optimal for this problem, as well as connections to lower bound techniques based on Wishart matrices developed in the matrix-vector query literature.

The Gradient Complexity Of Linear Regression

Abstract: We investigate the computational complexity of several basic linear algebra primitives, including largest eigenvector computation and linear regression, in the computational model that allows access to the data via a matrix-vector product oracle. We show that for polynomial accuracy, $\Theta(d)$ calls to the oracle are necessary and sufficient even for a randomized algorithm. ::: Our lower bound is based on a reduction to estimating the least eigenvalue of a random Wishart matrix. This simple distribution enables a concise proof, leveraging a few key properties of the random Wishart ensemble.


The Traj++ EWTA+reweighting #REFR performs best on the average ADE/FDE, but its performance gains on tailed samples are relatively little.

For long-tailed classification methods #OTHEREFR , we construct a classification head after the encoder of Traj++ EWTA to use it to classify the trajectories according to the discretization of Kalman filter errors, same as Makansi et al.

#OTHEREFR , and the classification loss is trained along with the prediction loss.

Table 1 summarizes our experimental results on ETH-UCY using a best-of-20 evaluation #OTHEREFR .

We can see that our method stably outperforms all comparing methods on all the top 1% − 5% long-tail samples.

Specifically, our framework outperforms the second best method: Traj++ EWTA+contrastive #OTHEREFR by 9.5% on ADE and 8.5% on FDE on the top 1% hardest samples, and maintains the average ADE and FDE nearly stable.

The Traj++ EWTA+resampling #OTHEREFR gets more gains on the most tailed samples, but its average ADE/FDE become much worse.

Unlike simply doing resampling or loss reweighting, hypernetwork can decouple head samples and tail samples in the parameter space of decoder, therefore achieves better performances.

Quantitative comparisons on Traj++ EWTA on Nuscenes.

Comparison results with the previous best long-tail prediction method #OTHEREFR on Nuscenes are in Table 2.

We find out that the resampling operation in the original Traj++ EWTA does not work well with FEND, probably because of causing overfit on hypernetwork.

Fend: A Future Enhanced Distribution-Aware Contrastive Learning Framework For Long-Tail Trajectory Prediction

Abstract: Predicting the future trajectories of the traffic agents is a gordian technique in autonomous driving. However, trajectory prediction suffers from data imbalance in the prevalent datasets, and the tailed data is often more complicated and safety-critical. In this paper, we focus on dealing with the long-tail phenomenon in trajectory prediction. Previous methods dealing with long-tail data did not take into account the variety of motion patterns in the tailed data. In this paper, we put forward a future enhanced contrastive learning framework to recognize tail trajectory patterns and form a feature space with separate pattern clusters.Furthermore, a distribution aware hyper predictor is brought up to better utilize the shaped feature space. Our method is a model-agnostic framework and can be plugged into many well-known baselines. Experimental results show that our framework outperforms the state-of-the-art longtail prediction method on tailed samples by 9.5% on ADE and 8.5% on FDE, while maintaining or slightly improving the averaged performance. Our method also surpasses many long-tail techniques on trajectory prediction task.

Class-Balanced Loss Based On Effective Number Of Samples

Abstract: With the rapid increase of large-scale, real-world datasets, it becomes critical to address the problem of long-tailed data distribution (i.e., a few classes account for most of the data, while most classes are under-represented). Existing solutions typically adopt class re-balancing strategies such as re-sampling and re-weighting based on the number of observations for each class. In this work, we argue that as the number of samples increases, the additional benefit of a newly added data point will diminish. We introduce a novel theoretical framework to measure data overlap by associating with each sample a small neighboring region rather than a single point. The effective number of samples is defined as the volume of samples and can be calculated by a simple formula $(1-\beta^{n})/(1-\beta)$, where $n$ is the number of samples and $\beta \in [0,1)$ is a hyperparameter. We design a re-weighting scheme that uses the effective number of samples for each class to re-balance the loss, thereby yielding a class-balanced loss. Comprehensive experiments are conducted on artificially induced long-tailed CIFAR datasets and large-scale datasets including ImageNet and iNaturalist. Our results show that when trained with the proposed class-balanced loss, the network is able to achieve significant performance gains on long-tailed datasets.


Therefore, recent methods rely on the so-called manifold hypothesis #REFR , stating that even complex and high-dimensional datasets are contained in a low-dimensional manifold.

The treatment of high-dimensional data is often computationally costly and numerically unstable.

Therefore, in many applications, it is important to find a low-dimensional representation of high-dimensional datasets.

Classical methods, like the principal component analysis (PCA) #OTHEREFR , assume that the data is contained in a low-dimensional subspace.

However, for complex datasets this assumption appears to be too restrictive, particularly when working with image datasets.

Based on this hypothesis, in recent years, many successful approaches have been based on generative models, able to represent high dimensional data in R n by a generator D : R d → R n with d n: these include generative adversarial networks (GANs) #OTHEREFR , variational autoencoders (VAEs) #OTHEREFR , injective flows #OTHEREFR and score-based diffusion models #OTHEREFR .

For a survey on older approaches to manifold learning, the reader is referred to #OTHEREFR and to the references therein.

Learning manifolds with multiple charts.

Under the assumption that D is injective, the set of generated points {D(z) : z ∈ R d } forms a manifold that approximates the training set.

Manifold Learning By Mixture Models Of Vaes For Inverse Problems

Abstract: Representing a manifold of very high-dimensional data with generative models has been shown to be computationally efficient in practice. However, this requires that the data manifold admits a global parameterization. In order to represent manifolds of arbitrary topology, we propose to learn a mixture model of variational autoencoders. Here, every encoder-decoder pair represents one chart of a manifold. We propose a loss function for maximum likelihood estimation of the model weights and choose an architecture that provides us the analytical expression of the charts and of their inverses. Once the manifold is learned, we use it for solving inverse problems by minimizing a data fidelity term restricted to the learned manifold. To solve the arising minimization problem we propose a Riemannian gradient descent algorithm on the learned manifold. We demonstrate the performance of our method for low-dimensional toy examples as well as for deblurring and electrical impedance tomography on certain image manifolds.

Representation Learning: A Review And New Perspectives

Abstract: The success of machine learning algorithms generally depends on data representation, and we hypothesize that this is because different representations can entangle and hide more or less the different explanatory factors of variation behind the data. Although specific domain knowledge can be used to help design representations, learning with generic priors can also be used, and the quest for AI is motivating the design of more powerful representation-learning algorithms implementing such priors. This paper reviews recent work in the area of unsupervised feature learning and deep learning, covering advances in probabilistic models, autoencoders, manifold learning, and deep networks. This motivates longer term unanswered questions about the appropriate objectives for learning good representations, for computing representations (i.e., inference), and the geometrical connections between representation learning, density estimation, and manifold learning.

low-dimensional manifold
high-dimensional datasets


This paper combines traditional Gabor waveletbased iris coding with a DNN driven by post-mortem iris data for feature extraction, reducing the recognition error rate by one-third compared to the baseline method #REFR .

The two respective feature maps are combined into a single feature map reflecting the differences between domains, which is then fed into a subsequent convolutional layer to extract high-level semantic information further.

The EER values of this research method on Q-FIRE and CASIA are 0.15% and 0.31%, respectively.

The iris recognition technique proposed by #OTHEREFR uses a local circular Gabor filter for initial feature extraction before input to the CNN to retain all directional information.

This design solves the problem of traditional Gabor wavelet transform insensitivity to circular orientation versus the difficulty of neural networks extracting directional features on the circular structure of the iris.

#OTHEREFR proposes an iris recognition method explicitly designed for post-mortem samples, thus enabling the application of iris biometrics in forensic science.

In addition to filters, attention mechanisms and feature histogram methods are also applied to optimize the feature extraction process of IR.

#OTHEREFR proposes a spatial attention feature fusion module to fuse features at different levels.

Spatial attention #OTHEREFR can encode the different positions of each importance in the feature map.

The fact that iris features are local features suggests that iris features have other spatial significance in different local regions; therefore, spatial attention feature fusion is well suited for iris recognition.

The spatial attention feature fusion module in the dual spatial attention network proposed in this study can learn the weights of each location and effectively fuse features at different levels.

Deep Learning For Iris Recognition: A Review

Abstract: Iris recognition is a secure biometric technology known for its stability and privacy. With no two irises being identical and little change throughout a person's lifetime, iris recognition is considered more reliable and less susceptible to external factors than other biometric recognition methods. Unlike traditional machine learning-based iris recognition methods, deep learning technology does not rely on feature engineering and boasts excellent performance. This paper collects 120 relevant papers to summarize the development of iris recognition based on deep learning. We first introduce the background of iris recognition and the motivation and contribution of this survey. Then, we present the common datasets widely used in iris recognition. After that, we summarize the key tasks involved in the process of iris recognition based on deep learning technology, including identification, segmentation, presentation attack detection, and localization. Finally, we discuss the challenges and potential development of iris recognition. This review provides a comprehensive sight of the research of iris recognition based on deep learning.

Iris Recognition After Death

Abstract: This paper presents a comprehensive study of post-mortem human iris recognition carried out for 1200 near-infrared and 1787 visible-light samples collected from 37 deceased individuals kept in mortuary conditions. We used four independent iris recognition methods (three commercial and one academic) to analyze genuine and impostor comparison scores and check the dynamics of iris quality decay over a period of up to 814 h after death. This study shows that post-mortem iris recognition may be close-to-perfect approximately 5–7 h after death and occasionally is still viable even 21 days after death. These conclusions contradict the statements present in the past literature that the iris is unusable as a biometrics shortly after death, and show that the dynamics of post-mortem changes to the iris that are important for biometric identification are more moderate than previously hypothesized. This paper contains a thorough medical commentary that helps to understand which post-mortem metamorphoses of the eye may impact the performance of automatic iris recognition. An important finding is that false-match probability is higher when live iris images are compared with post-mortem samples than when only live samples are used in comparisons. This paper conforms to reproducible research and the database used in this study is made publicly available to facilitate research on post-mortem iris recognition. To the best of our knowledge, this paper offers the most comprehensive evaluation of post-mortem iris recognition and the largest database of post-mortem iris images.


For instance, an AGI agent shall be capable of understanding, learning, and carrying out any intellectual work that a human person is capable of #REFR .

AGI usually refers to machine intelligence that possesses human-like cognitive abilities.

In contrast, to narrow/limited AI, which is created to excel in specific tasks or domains, AGI systems mimic humans' general-purpose problem-solving abilities #OTHEREFR .

AGI differs from limited AI in several significant ways, demonstrating the general objective of reaching humanlike intelligence in AI systems.

The ability of AGI systems to function autonomously, making judgments and conducting actions without the need for ongoing human supervision, is one of these features.

Thanks to this degree of autonomy, AGI may work well in complicated, dynamic situations, enabling it to adjust to unforeseen conditions #OTHEREFR .

AGI also has the capacity for general-purpose learning, allowing it to reason and learn across various areas.

Artificial General Intelligence (Agi) For Education

Abstract: Artificial general intelligence (AGI) has gained global recognition as a future technology due to the emergence of breakthrough large language models and chatbots such as GPT-4 and ChatGPT, respectively. AGI aims to replicate human intelligence through computer systems, which is one of the critical technologies having the potential to revolutionize the field of education. Compared to conventional AI models, typically designed for a limited range of tasks, demand significant amounts of domain-specific data for training and may not always consider intricate interpersonal dynamics in education. AGI, driven by the recent large pre-trained models, represents a significant leap in the capability of machines to perform tasks that require human-level intelligence, such as reasoning, problem-solving, decision-making, and even understanding human emotions and social interactions. This work reviews AGI's key concepts, capabilities, scope, and potential within future education, including setting educational goals, designing pedagogy and curriculum, and performing assessments. We also provide rich discussions over various ethical issues in education faced by AGI and how AGI will affect human educators. The development of AGI necessitates interdisciplinary collaborations between educators and AI engineers to advance research and application efforts.

A Collection Of Definitions Of Intelligence

Abstract: This chapter is a survey of a large number of informal definitions of “intelligence” that the authors have collected over the years. Naturally, compiling a complete list would be impossible as many definitions of intelligence are buried deep inside articles and books. Nevertheless, the 70 odd definitions presented here are, to the authors' knowledge, the largest and most well referenced collection there is.


A.2 Pseudo-Linear Time In The Regime With Positive Drift

We note that there is a non-vanishing gap between our upper and lower bound in the theorem above when = Ω #REFR .

Recall that this estimate was conditional on a fixed value of 0 .

Since 0 follows a binomial distribution with parameters and #OTHEREFR 2 , we have 0 ≥ 2 − 3/4 with probability 1− (1), and in this case,

where the last estimate exploits our assumption = ( ). Just from the contribution of this case, we

The reason, most likely, is the argument used in the lower bound proof that the Metropolis algorithm cannot be faster than randomized local search, which ignores any negative effect of accepting inferior solutions.

For our purposes, the theorem above is sufficient, since for all but very large values of (where the gap is negligible) the runtime of the Metropolis algorithm is dominated by the second part of the optimization process starting from a solution with ( ) = .

The reason why we could not prove a tighter bound for all values of is that the existing multiplicative drift theorems for lower bounds, e.g., Theorem 2.2 in #OTHEREFR or Theorem 3.7 in #OTHEREFR , either are not applicable to our process or necessarily lead to a constant-factor gap to the upper bound obtained from multiplicative drift.

Applying the variable drift theorem from #OTHEREFR to the process = min{ | ≤ } appears to be a promising way to overcome these difficulties, but since we do not need such a precise bound, we do not follow this route any further.

How Well Does The Metropolis Algorithm Cope With Local Optima? *

Abstract: The Metropolis algorithm (MA) is a classic stochastic local search heuristic. It avoids getting stuck in local optima by occasionally accepting inferior solutions. To better and in a rigorous manner understand this ability, we conduct a mathematical runtime analysis of the MA on the CLIFF benchmark. Apart from one local optimum, cliff functions are monotonically increasing towards the global optimum. Consequently, to optimize a cliff function, the MA only once needs to accept an inferior solution. Despite seemingly being an ideal benchmark for the MA to profit from its main working principle, our mathematical runtime analysis shows that this hope does not come true. Even with the optimal temperature (the only parameter of the MA), the MA optimizes most cliff functions less efficiently than simple elitist evolutionary algorithms (EAs), which can only leave the local optimum by generating a superior solution possibly far away. This result suggests that our understanding of why the MA is often very successful in practice is not yet complete. Our work also suggests to equip the MA with global mutation operators, an idea supported by our preliminary experiments. CCS CONCEPTS • Theory of computation → Theory of randomized search heuristics.

A Tight Runtime Analysis For The (Μ + Λ) Ea

Abstract: Despite significant progress in the theory of evolutionary algorithms, the theoretical understanding of true population-based evolutionary algorithms remains challenging and only few rigorous results exist. Already for the most basic problem, the determination of the asymptotic runtime of the (μ + λ) evolutionary algorithm on the simple OneMax benchmark function, only the special cases μ = 1 and λ = 1 have been solved. In this work, we analyze this long-standing problem and show the asymptotically tight result that the runtime T, the number of iterations until the optimum is found, satisfies [EQUATION] where log+ x := max{1, log x} for all x > 0.


In addition, most existing approaches are not applicable when the IoT devices are behind a NAT (network address translation) enabled router, as many of the features get altered in the NAT process #REFR .

This can help organizations effectively manage the security issues associated with IoT devices and determine whether the behavior/activity of the connected IoT devices is normal.

Existing approaches & their limitations: Prior research suggested ways of identifying IoT devices by analyzing the network communication (Meidan et al., 2017a,b; #OTHEREFR .

Since the methods proposed are based on machine learning, feature engineering (i.e., feature extraction, selection, and tuning) is required.

This necessitates manual input from subject matter experts, which is both expensive and prone to errors.

Existing approaches are timeconsuming, requiring multiple sessions to identify known and unknown (also referred to as unauthorized in this paper) IoT devices, and tend to have complex architecture, since they use multistage models.

Proposed solution: Our approach mitigates these limitations.

Just a single session is needed to identify known and unknown IoT devices; in addition, it is free from the burden of feature engineering and the errors that are accompanied with feature engineering, and it has a simple architecture.

The proposed approach also applies to IoT devices behind a NAT-enabled router, as the NAT process does not alter the payload of the communication.

The proposed approach enables us to identify known and unknown IoT devices in the network in various scenarios.

Organizations' use of DHCP and the ease in which MAC addresses can be spoofed has made it difficult to identify IoT devices using traditional approaches #OTHEREFR .

Iot Device Identification Based On Network Communication Analysis Using Deep Learning

Abstract: Attack vectors for adversaries have increased in organizations because of the growing use of less secure IoT devices. The risk of attacks on an organization's network has also increased due to the bring your own device (BYOD) policy which permits employees to bring IoT devices onto the premises and attach them to the organization's network. To tackle this threat and protect their networks, organizations generally implement security policies in which only white-listed IoT devices are allowed on the organization's network. To monitor compliance with such policies, it has become essential to distinguish IoT devices permitted within an organization's network from non-whitelisted (unknown) IoT devices. In this research, deep learning is applied to network communication for the automated identification of IoT devices permitted on the network. In contrast to existing methods, the proposed approach does not require complex feature engineering of the network communication, because the 'communication behavior' of IoT devices is represented as small images which are generated from the device's network communication payload. The proposed approach is applicable for any IoT device, regardless of the protocol used for communication. As our approach relies on the network communication payload, it is also applicable for the IoT devices behind a network address translation (NAT) enabled router. In this study, we trained various classifiers on a publicly accessible dataset to identify IoT devices in different scenarios, including the identification of known and unknown IoT devices, achieving over 99% overall average detection accuracy.

Privacy-Preserving Detection Of Iot Devices Connected Behind A Nat In A Smart Home Setup

Abstract: Today, telecommunication service providers (telcos) are exposed to cyber-attacks executed by compromised IoT devices connected to their customers' networks. Such attacks might have severe effects not only on the target of attacks but also on the telcos themselves. To mitigate those risks we propose a machine learning based method that can detect devices of specific vulnerable IoT models connected behind a domestic NAT, thereby identifying home networks that pose a risk to the telco's infrastructure and availability of services. As part of the effort to preserve the domestic customers' privacy, our method relies on NetFlow data solely, refraining from inspecting the payload. To promote future research in this domain we share our novel dataset, collected in our lab from numerous and various commercial IoT devices.


Typically, a UNet #REFR is used to learn to produce the denoising signal. Conditioning e.g.

Recently diffusion model #OTHEREFR have shown superior image quality and text-guided capability.

In training, the diffusion model gradually adds noise to the image until it becomes random noise; this process is known as forward diffusion.

The diffused random noise act as latent variables and is denoised progressively to generate an image in image sampling; this progress is known as reverse diffusion.

text can be applied using classifier #OTHEREFR or classified-free approaches #OTHEREFR .

Most methods #OTHEREFR use the latter to find the direction between the conditional and unconditional in the latent space, which to be applied in sampling time to guide the model towards the conditioning direction.

However, denoising every image pixel can be computationally expensive; therefore, LDM #OTHEREFR proposed a two-stage process.

It first trained a variational autoencoder (VAE) #OTHEREFR to encode the image into smaller dimensional latent variables, and the diffusion model learned to produce the VAE latent variables.

Diffusion models could provide image editing by performing text-guided diffusion on regions defined by segmentation mask #OTHEREFR .Dreambooth #OTHEREFR shows that they could encode a person's face into a text token and use a diffusion model to generate the person in a different scene.

Upgpt: Universal Diffusion Model For Person Image Generation, Editing And Pose Transfer

Abstract: Existing person image generative models can do either image generation or pose transfer but not both. We propose a unified diffusion model, UPGPT to provide a universal solution to perform all the person image tasks-generative, pose transfer, and editing. With fine-grained multimodality and disentanglement capabilities, our approach offers finegrained control over the generation and the editing process of images using a combination of pose, text, and image, all without needing a semantic segmentation mask which can be challenging to obtain or edit. We also pioneer the parameterized body SMPL model in pose-guided person image generation to demonstrate new capability-simultaneous pose and camera view interpolation while maintaining a person's appearance. Results on the benchmark Deep-Fashion dataset show that UPGPT is the new state-of-theart while simultaneously pioneering new capabilities of edit and pose transfer in human image generation.

U-Net: Convolutional Networks For Biomedical Image Segmentation

Abstract: There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently. The architecture consists of a contracting path to capture context and a symmetric expanding path that enables precise localization. We show that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks. Using the same network trained on transmitted light microscopy images (phase contrast and DIC) we won the ISBI cell tracking challenge 2015 in these categories by a large margin. Moreover, the network is fast. Segmentation of a 512x512 image takes less than a second on a recent GPU. The full implementation (based on Caffe) and the trained networks are available at http://lmb.informatik.uni-freiburg.de/people/ronneber/u-net .