Hypernetwork weights are initialized using Kaiming initialization #REFR on fan out mode and biases are initialized to zero.

We implement the hypernetwork as a neural network with fully connected layers and LeakyReLU activations #OTHEREFR on all but the last layer which has linear output.

Hypernetwork inputs γ are sampled uniformly in the range [0,1].

Unless specified otherwise, the hypernetwork is modeled with two hidden layers with 16 and 128 neurons respectively.

We treat the hypernetwork output as a set of separate fully connected layers each one corresponding to a different weight tensor of the primary network.

We initialize each one of these layers independently, only considering their respective input and output dimensions.

In a similar fashion, we initialize the parameters θ 0 as separate subsets to take into account the corresponding weight tensor dimensions of the primary network.

The choice of primary network varies among the considered task, full details are included in Appendix A.

Non-Proportional Parametrizations For Stable Hypernetwork Learning

Abstract: Hypernetworks are neural networks that generate the parameters of another neural network. In many scenarios current hypernetwork training strategies are unstable, and convergence is often far slower than for non-hypernetwork models. We show that this problem is linked to an issue that arises when using common choices of hypernetwork architecture and initialization. We demonstrate analytically and experimentally how this numerical issue can lead to an instability during training that slows, and sometimes even prevents, convergence. We also demonstrate that popular deep learning normalization strategies fail to address these issues. We then propose a solution to the problem based on a revised hypernetwork formulation that uses non-proportional additive parametrizations. We test the proposed reparametrization on several tasks, and demonstrate that it consistently leads to more stable training, achieving faster convergence.

Delving Deep Into Rectifiers: Surpassing Human-Level Performance On Imagenet Classification

Abstract: Rectified activation units (rectifiers) are essential for state-of-the-art neural networks. In this work, we study rectifier neural networks for image classification from two aspects. First, we propose a Parametric Rectified Linear Unit (PReLU) that generalizes the traditional rectified unit. PReLU improves model fitting with nearly zero extra computational cost and little overfitting risk. Second, we derive a robust initialization method that particularly considers the rectifier nonlinearities. This method enables us to train extremely deep rectified models directly from scratch and to investigate deeper or wider network architectures. Based on the learnable activation and advanced initialization, we achieve 4.94% top-5 test error on the ImageNet 2012 classification dataset. This is a 26% relative improvement over the ILSVRC 2014 winner (GoogLeNet, 6.66% [33]). To our knowledge, our result is the first to surpass the reported human-level performance (5.1%, [26]) on this dataset.


The joint velocities thereby exhibit a bang-bang type of behavior (generating joint velocities at the limits) for as fast as possible task convergence #REFR .

At around 36 s the augmentation of the level 8 position task stops as the right hand reached its prescribed position, see Fig. 20 .

The right hand then follows a target swinging from the front [3, −1.5, 2] m to the back [−3, −1.5, 0] m of the robot (for reference, HRP-2Kai is 1.71 m tall with a 2.11 m arm span).

The target is always out of reach but numerically stable robot control is obtained thanks to the switch to the Newton's method.

As can be seen from the middle and bottom graph of Fig.

22 , the joint velocities are numerically stable and do not show signs of high frequency oscillations as for example observed in Fig. 14 for the GN algorithm.

During the stretch motions, the CoM is well outside the support polygon of the feet (approximately at [±0.1, ±0.2, ±∞] m) which stresses the importance of the left hand supporting contact (see Fig. 19 ).

Figure 23 shows the norms of the tracking error of the right hand during the stretch motions.

During forward stretches LexDynAH performs better than WLS (Err WLS −Err LexDynAH > 0).

Especially during the second forward stretch (130s+) LexDynAH gets over 0.05 m closer to the target than WLS due to fully stretching its arm leading to a kinematic singularity.

LexDynAH has a higher error norm than WLS during the backward stretch (see Fig. 23 , 100 s to 117 s).

The Hierarchical Newton'S Method For Numerically Stable Prioritized Dynamic Control

Abstract: This work links optimization approaches from hierarchical least-squares programming to instantaneous prioritized whole-body robot control. Concretely, we formulate the hierarchical Newton's method which solves prioritized non-linear least-squares problems in a numerically stable fashion even in the presence of kinematic and algorithmic singularities of the approximated kinematic constraints. These results are then transferred to control problems which exhibit the additional variability of time. This is necessary in order to formulate acceleration based controllers and to incorporate the second order dynamics. However, we show that the Newton's method without complicated adaptations is not appropriate in the acceleration domain. We therefore formulate a velocity based controller which exhibits second order proportional derivative convergence characteristics. Our developments are verified in toy robot control scenarios as well as in complex robot experiments which stress the importance of prioritized control and its singularity resolution.

A General, Fast, And Robust Implementation Of The Time-Optimal Path Parameterization Algorithm

Abstract: Finding the time-optimal parameterization of a given path subject to kinodynamic constraints is an essential component in many robotic theories and applications. The objective of this paper is to provide a general, fast, and robust implementation of this component. For this, we give a complete solution to the issue of dynamic singularities, which are the main cause of failure in existing implementations. We then present an open-source implementation of the algorithm in C++/Python and demonstrate its robustness and speed in various robotics settings.


We obtain the camera pose for the input image using off-the-shelf face pose estimator #REFR .

because of inconsistency in the 2D super-resolution module.

In contrast, our method retains the quality in out-of-training camera distribution.

9 , we provide the monocular fitting results to a given input image.

Given an input image, we use our trained model to fit to the input image and show reconstruction and novel view renderings.

Then we optimize the latent vector w for about 1200 iterations using reconstruction loss and simultaneously optimize the generator parameter and latent vector for another 800 iterations to get the better fitting. In the same Fig.

9 , we provide a comparison with Authentic Volumetric Avatars(AVA) #OTHEREFR .

While AVA is not a generative model, it can generate a volumetric avatar using a phone scan, we thus qualitatively compare with this method as well.

Please note AVA is not a main comparison, as this method targets expression retargeting, while we don't.

#OTHEREFR heavily relies on the tracked mesh to place the primitives and suffers in the case of long hair, which results in truncated results.

Gvp: Generative Volumetric Primitives

Abstract: Advances in 3D-aware generative models have pushed the boundary of image synthesis with explicit camera control. To achieve high-resolution image synthesis, several attempts have been made to design efficient generators, such as hybrid architectures with both 3D and 2D components. However, such a design compromises multiview consistency, and the design of a pure 3D generator with high resolution is still an open problem. In this work, we present Generative Volumetric Primitives (GVP), the first pure 3D generative model that can sample and render 512resolution images in real-time. GVP jointly models a number of volumetric primitives and their spatial information, both of which can be efficiently generated via a 2D convolutional network. The mixture of these primitives naturally captures the sparsity and correspondence in the 3D volume. The training of such a generator with high degree of freedom is made possible through a knowledge distillation technique. Experiments on several datasets demonstrate superior efficiency and 3D consistency of GVP over the state-ofthe-art.

Accurate 3D Face Reconstruction With Weakly-Supervised Learning: From Single Image To Image Set

Abstract: Recently, deep learning based 3D face reconstruction methods have shown promising results in both quality and efficiency.However, training deep neural networks typically requires a large volume of data, whereas face images with ground-truth 3D face shapes are scarce. In this paper, we propose a novel deep 3D face reconstruction approach that 1) leverages a robust, hybrid loss function for weakly-supervised learning which takes into account both low-level and perception-level information for supervision, and 2) performs multi-image face reconstruction by exploiting complementary information from different images for shape aggregation. Our method is fast, accurate, and robust to occlusion and large pose. We provide comprehensive experiments on three datasets, systematically comparing our method with fifteen recent methods and demonstrating its state-of-the-art performance.


Replica Dataset (https://github.com/facebookresearch/Repl ica-Dataset) is a dataset of high-quality 3D reconstructions of indoor environments, including RGB-D images, semantic annotations, and camera poses #REFR .

Freiburg Forest Dataset (http://deepscene.cs.uni-freiburg.de/) contains RGB, depth, and thermal images captured in a forest environment using a custom sensor setup #OTHEREFR .

3D60: Indoor Scene Understanding in 3D (https://vcl3d.gith ub.io/3D60/) is a dataset containing 60 indoor scenes with 360°panoramas, depth maps, and semantic annotations #OTHEREFR .

The dataset has high-quality images and depth maps, suitable for evaluating 3D reconstruction, scene understanding, and navigation tasks, diverse set of scenes with varying object types and room layouts.

Taskonomy Dataset (http://taskonomy.stanford.edu/) is a large scale dataset that contains 4 million images from 20,000 scenes #OTHEREFR .

The dataset contains 3D point clouds, surface normals, and semantic annotations.

SCARED dataset (The stereo correspondence and reconstruction of endoscopic data) (https://endovissub2019-scared.gran d-challenge.org/) is a medical dataset captured using a da Vinci Xi surgical robot.

The dataset consists of 7 training datasets and 2 test datasets, and each of the dataset corresponds to a single porcine subject, in which an endoscope was moved using the da Vinci Xi to observe scenes, and the camera's pose relative to the fixed robot base was recorded based on robot kinematic status #OTHEREFR .

EndoSLAM dataset (https://github.com/CapsuleEndoscope/En doSLAM) is a medical dataset that consists of both ex-vivo and synthetically generated data.

The ex-vivo part of the dataset includes standard as well as capsule endoscopy recordings. The dataset is divided into 35 sub-datasets.

Specifically, 18, 5 and 12 sub-datasets exist for colon, small intestine and stomach respectively #OTHEREFR .

3 Dimensional Dense Reconstruction: A Review Of Algorithms And Dataset

Abstract: 3D dense reconstruction refers to the process of obtaining the complete shape and texture features of 3D objects from 2D planar images. 3D reconstruction is an important and extensively studied problem, but it is far from being solved. This work systematically introduces classical methods of 3D dense reconstruction based on geometric and optical models, as well as methods based on deep learning. It also introduces datasets for deep learning and the performance and advantages and disadvantages demonstrated by deep learning methods on these datasets.

The Replica Dataset: A Digital Replica Of Indoor Spaces

Abstract: We introduce Replica, a dataset of 18 highly photo-realistic 3D indoor scene reconstructions at room and building scale. Each scene consists of a dense mesh, high-resolution high-dynamic-range (HDR) textures, per-primitive semantic class and instance information, and planar mirror and glass reflectors. The goal of Replica is to enable machine learning (ML) research that relies on visually, geometrically, and semantically realistic generative models of the world - for instance, egocentric computer vision, semantic segmentation in 2D and 3D, geometric inference, and the development of embodied agents (virtual robots) performing navigation, instruction following, and question answering. Due to the high level of realism of the renderings from Replica, there is hope that ML systems trained on Replica may transfer directly to real world image and video data. Together with the data, we are releasing a minimal C++ SDK as a starting point for working with the Replica dataset. In addition, Replica is `Habitat-compatible', i.e. can be natively used with AI Habitat for training and testing embodied agents.

high-quality 3D reconstructions
Replica Dataset


CFS-BA has performed well for CIC-IDS2017, but does not have CSE-CIC-IDS2018 results and lacks results for DNN and RF models #REFR .

This methodology performs much faster than any embedded method, and can give more intuitive reasoning into the decision making process, leading us to choose it.

The only issue is that we cannot explore every possible subset, and as such need to use an optimizer method to find the closest subset to the global maxima score.

Bat Algorithm (BA) was proposed by Yang in 2010 as a metaheuristic optimization algorithm to explore a space mimicking the behavior of microbats using echolocation #OTHEREFR .

Most metaheuristic algorithms have two phases; exploration attempts to discover the widest range of the feature space, and exploitation aims to discover the best local solution within a given region.

Unlike most modern metaheuristic algorithms, BA gradually switches between exploration and exploitation using a self-tuning hyperparameter #OTHEREFR .

The Aquila Optimizer was introduced as a faster and potentially more efficient metaheuristic algorithm than prior methodology.

While slower to converge onto a maxima, it has been shown to outperform BA when used in feature selection on cybersecurity benchmark datasets #OTHEREFR .

Because of its promising nature, the algorithm will be used and compared to BA with CFS scoring.

AO has performed well for CIC-IDS2017, but used a different fitness than CFS and does not have CSE-CIC-IDS2018 results #OTHEREFR .

The paper is organized as follows: our motivation for the particular fields, tools, and algorithms used are in section I, particular mathematical and technical details regarding the implementation of our research are in section II, information specific to our dataset and its distribution and processing are in section III, experimental results are in section IV, interpretation of results and limitations of the study are in section V, and a summary of the work's main achievements as well as future work are in section VI.

Feature Reduction Method Comparison Towards Explainability And Efficiency In Cybersecurity Intrusion Detection Systems

Abstract: In the realm of cybersecurity, intrusion detection systems (IDS) detect and prevent attacks based on collected computer and network data. In recent research, IDS models have been constructed using machine learning (ML) and deep learning (DL) methods such as Random Forest (RF) and deep neural networks (DNN). Feature selection (FS) can be used to construct faster, more interpretable, and more accurate models. We look at three different FS techniques; RF information gain (RF-IG), correlation feature selection using the Bat Algorithm (CFS-BA), and CFS using the Aquila Optimizer (CFS-AO). Our results show CFS-BA to be the most efficient of the FS methods, building in 55% of the time of the best RF-IG model while achieving 99.99% of its accuracy. This reinforces prior contributions attesting to CFS-BA's accuracy while building upon the relationship between subset size, CFS score, and RF-IG score in final results.

Building An Efficient Intrusion Detection System Based On Feature Selection And Ensemble Classifier

Abstract: Since Internet is so popular and prevailing in human life, countering cyber threats, especially attack detection, is a challenging area of research in the field of cyber security. Intrusion detection systems (IDSs) are essential entities in a network topology aiming to safeguard the integrity and availability of sensitive assets in the protected systems. Although many supervised and unsupervised learning approaches from the field of machine learning and pattern recognition have been used to increase the efficacy of IDSs, it is still a problem to deal with lots of redundant and irrelevant features in high-dimension datasets for network anomaly detection. To this end, we propose a novel methodology combining the benefits of correlation-based feature selection(CFS) and bat algorithm(BA) with an ensemble classifier based on C4.5, Random Forest(RF), and Forest by Penalizing Attributes(Forest PA), which can be able to classify both common and rare types of attacks with high accuracy and efficiency. The experimental results, using a novel intrusion detection dataset, namely CIC-IDS2017, reveal that our CFS-BA-Ensemble method is able to contribute more critical features and significantly outperforms individual approaches, achieving high accuracy and low false alarm rate. Moreover, compared with the majority of the existing state-of-the-art and legacy techniques, our approach exhibits better performance under several classification metrics in the context of classification accuracy, f-measure, attack detection rate, and false alarm rate.


The implementation used the Python library Ray RLLib #REFR for implementations of the reinforcement learning algorithms.

Three experiments were performed to evaluate the simulator and defender agent: A comparison between the RL policy and heuristic policies at different levels of IDS accuracy, a comparison between RL policies trained with different attackers and an analysis of how the policy training is affected as the graph size increases.

RL policy training and evaluation was performed on a Google Cloud virtual machine equipped with an Nvidia V100 GPU, 12 CPU cores, and 30 GB of RAM.

All policies trained with RL were run for 500 PPO policy iterations. All policies were evaluated by running 500 episodes.

Each policy took roughly 20 minutes to train and evaluate.

Experiments were performed three times with different seeds for the random number generator.

The neural networks used two hidden layers with 128 nodes.

All TTC values except those explicitly set to 0 were initialized at the start of each episode by sampling from the exponential distribution f (x; β) = 1 β e − 1 β x , where β is the mean TTC value assigned to each attack step.

Learning Automated Defense Strategies Using Graph-Based Cyber Attack Simulations

Abstract: We implemented and evaluated an automated cyber defense agent. The agent takes security alerts as input and uses reinforcement learning to learn a policy for executing predefined defensive measures. The defender policies were trained in an environment intended to simulate a cyber attack. In the simulation, an attacking agent attempts to capture targets in the environment, while the defender attempts to protect them by enabling defenses. The environment was modeled using attack graphs based on the Meta Attack Language language. We assumed that defensive measures have downtime costs, meaning that the defender agent was penalized for using them. We also assumed that the environment was equipped with an imperfect intrusion detection system that occasionally produces erroneous alerts based on the environment state. To evaluate the setup, we trained the defensive agent with different volumes of intrusion detection system noise. We also trained agents with different attacker strategies and graph sizes. In experiments, the defensive agent using policies trained with reinforcement learning outperformed agents using heuristic policies. Experiments also demonstrated that the policies could generalize across different attacker strategies. However, the performance of the learned policies decreased as the attack graphs increased in size.

Rllib: Abstractions For Distributed Reinforcement Learning

Abstract: Reinforcement learning (RL) algorithms involve the deep nesting of highly irregular computation patterns, each of which typically exhibits opportunities for distributed computation. We argue for distributing RL components in a composable way by adapting algorithms for top-down hierarchical control, thereby encapsulating parallelism and resource requirements within short-running compute tasks. We demonstrate the benefits of this principle through RLlib: a library that provides scalable software primitives for RL. These primitives enable a broad range of algorithms to be implemented with high performance, scalability, and substantial code reuse. RLlib is available at this https URL.


We refer the reader to #REFR for further details and discussion of gated multi-head attention blocks.

This first attention block's output form the 'queries', and along with the 'keys' and 'values' from the spatial cache, make up the inputs to the gated multi-head attention block #OTHEREFR .

In this way, temporal features can attend to spatial features and learn complementary cross-modality information.

Furthermore, with gated multi-head attention, temporal attention scores respectively scale the corresponding attention scores of spatial cache elements.

For instance, if an image receives high attention in the first, temporal attention block, its corresponding subpixels will receive higher attention in this gated, spatial attention block.

This is a mechanism to ensure high temporal attention translates to high spatial attention.

Finally, the output of this gated, multi-head attention block (after another residual addition and layer normalization) form the inputs for two separate, feed forward heads: one for the policy and one for the classifier.

Early Classifying Multimodal Sequences

Abstract: Often pieces of information are received sequentially over time. When did one collect enough such pieces to classify? Trading wait time for decision certainty leads to early classification problems that have recently gained attention as a means of adapting classification to more dynamic environments. However, so far results have been limited to unimodal sequences. In this pilot study, we expand into early classifying multimodal sequences by combining existing methods. We show our new method yields experimental AUC advantages of up to 8.7%.

Omninet: A Unified Architecture For Multi-Modal Multi-Task Learning

Abstract: Transformer is a popularly used neural network architecture, especially for language understanding. We introduce an extended and unified architecture which can be used for tasks involving a variety of modalities like image, text, videos, etc. We propose a spatio-temporal cache mechanism that enables learning spatial dimension of the input in addition to the hidden states corresponding to the temporal input sequence. The proposed architecture further enables a single model to support tasks with multiple input modalities as well as asynchronous multi-task learning, thus we refer to it as OmniNet. For example, a single instance of OmniNet can concurrently learn to perform the tasks of part-of-speech tagging, image captioning, visual question answering and video activity recognition. We demonstrate that training these four tasks together results in about three times compressed model while retaining the performance in comparison to training them individually. We also show that using this neural network pre-trained on some modalities assists in learning an unseen task. This illustrates the generalization capacity of the self-attention mechanism on the spatio-temporal cache present in OmniNet.


The classification model is a fine tuned COVID-Twitter Pre-Trained (Müller, Salathé, and Kummervold 2020) BERT Large #REFR .

The training process of the model is based on Jiang et al.

2021, and we will briefly explain the key settings in this section.

For more detailed training settings, please refer to the paper.

It contains 24 transformer layers, and in our experiment, only the parameters in the last transformer encoding layer are unlocked for fine-tuning, while the remaining BERT weights remain frozen.

Similar to the BERT next sentence prediction task, the input to the model is constructed as ' We conduct five-folds 'Leave-Claim-Out Cross Validation' described in #OTHEREFR to compare enrichment performance.

Note that the five-folds are only conducted on the original Jiang et al. (2021) annotated dataset.

The enriched data is only added as supplemental training data, therefore, the total number of test samples is the same as in the experiment described in #OTHEREFR .

The purpose of Leave-Claim-Out Cross Validation is to test the classification performance for unseen misinformation.

A Large-Scale Comparative Study Of Accurate Covid-19 Information Versus Misinformation

Abstract: The COVID-19 pandemic led to an infodemic where an overwhelming amount of COVID-19 related content was being disseminated at high velocity through social media. This made it challenging for citizens to differentiate between accurate and inaccurate information about COVID-19. This motivated us to carry out a comparative study of the characteristics of COVID-19 misinformation versus those of accurate COVID-19 information through a large-scale computational analysis of over 242 million tweets. The study makes comparisons alongside four key aspects: 1) the distribution of topics, 2) the live status of tweets, 3) language analysis and 4) the spreading power over time. An added contribution of this study is the creation of a COVID-19 misinformation classification dataset. Finally, we demonstrate that this new dataset helps improve misinformation classification by more than 9% based on average F1 measure.

Bert: Pre-Training Of Deep Bidirectional Transformers For Language Understanding

Abstract: We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models, BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications. ::: BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5% (7.7% point absolute improvement), MultiNLI accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement).

fine tuned COVID-Twitter
Kummervold 2020) BERT


With the gradient of log probability density as score function #REFR , SGM generates the samples towards decreasing noise levels and trains the model by estimating the score functions for noisy data distribution.

Experimental results on multiple datasets show the effectiveness of DPM when estimating complex data distribution.

DPM #OTHEREFR can be viewed as the foundation of DDPM #OTHEREFR , while DDPM #OTHEREFR optimizes DPM #OTHEREFR with improved implementations.

Techniques for improving score-based generative models have also been investigated in #OTHEREFR , #OTHEREFR .

SGM #OTHEREFR proposes to perturb the data with random Gaussian noise of various magnitudes.

Despite different motivations, SGM shares a similar optimization objective with DDPM during training, which is also discussed in #OTHEREFR that the DDPM under a certain parameterization is equivalent to SGM during training.

A improved variant of SGM is investigated in #OTHEREFR for generalizing to high-resolution images.

Text-To-Image Diffusion Model In Generative Ai: A Survey

Abstract: This survey reviews text-to-image diffusion models in the context that diffusion models have emerged to be popular for a wide range of generative tasks. As a self-contained work, this survey starts with a brief introduction of how a basic diffusion model works for image synthesis, followed by how condition or guidance improves learning. Based on that, we present a review of state-of-the-art methods on text-conditioned image synthesis, i.e. text-to-image. We further summarize applications beyond text-to-image generation: text-guided creative generation and text-guided image editing. Beyond the progress made so far, we discuss existing challenges and promising future directions.

Generative Modeling By Estimating Gradients Of The Data Distribution

Abstract: We introduce a new generative model where samples are produced via Langevin dynamics using gradients of the data distribution estimated with score matching. Because gradients can be ill-defined and hard to estimate when the data resides on low-dimensional manifolds, we perturb the data with different levels of Gaussian noise, and jointly estimate the corresponding scores, i.e., the vector fields of gradients of the perturbed data distribution for all noise levels. For sampling, we propose an annealed Langevin dynamics where we use gradients corresponding to gradually decreasing noise levels as the sampling process gets closer to the data manifold. Our framework allows flexible model architectures, requires no sampling during training or the use of adversarial methods, and provides a learning objective that can be used for principled model comparisons. Our models produce samples comparable to GANs on MNIST, CelebA and CIFAR-10 datasets, achieving a new state-of-the-art inception score of 8.87 on CIFAR-10. Additionally, we demonstrate that our models learn effective representations via image inpainting experiments.


To address these shortcomings, in this work, we introduce Probabilistic Panoptic-DeepLab (ProPanDL), a family of networks that extend the Panoptic-DeepLab architecture #REFR to produce pixel-wise predictive uncertainty in panoptic segmentation.

To date, there has been little research exploring the application of uncertainty estimation to the panoptic segmentation task. Uncertainty-aware panoptic segmentation was introduced by Sirohi et al.

#OTHEREFR , who introduce a network architecture capable of producing pixel-wise uncertainties as well two metrics, pECE and uECE, for evaluating the task. However, their approach is not without limitations.

First, while panoptic segmentation has two aspects (semantic segmentation and instance discrimination), their proposed metrics evaluate only the total uncertainty rather than the two components independently.

Second, their EvPSNet segmentation network is capable of only producing scalar uncertainty scores in the range [0, 1], rather than full distributions.

This limitation is reflected in the uECE and pECE metrics, which evaluate only real-valued uncertainty rather than full distributions.

To our knowledge, this work is the first to predict full output distributions for either instance or panoptic segmentation.

ProPanDL uses separate semantic and spatial branches to jointly estimate semantic segmentation, instance discrimination, and their associated pixel-wise uncertainties.

Semantic segmentation uncertainty is represented as a calibrated categorical distribution, produced via Temperature Scaling #OTHEREFR or Evidential Deep Learning #OTHEREFR .

For instance discrimination, we apply Variance Networks #OTHEREFR and Sam-pleNet #OTHEREFR to estimate the distribution of pixels relative to their corresponding instance center.

In addition, we propose to decouple the semantic and spatial uncertainty evaluation in pECE.

Propandl: A Modular Architecture For Uncertainty-Aware Panoptic Segmentation

Abstract: We introduce ProPanDL, a family of networks capable of uncertainty-aware panoptic segmentation. Unlike existing segmentation methods, ProPanDL is capable of estimating full probability distributions for both the semantic and spatial aspects of panoptic segmentation. We implement and evaluate ProPanDL variants capable of estimating both parametric (Variance Network) and parameter-free (SampleNet) distributions quantifying pixel-wise spatial uncertainty. We couple these approaches with two methods (Temperature Scaling and Evidential Deep Learning) for semantic uncertainty estimation. To evaluate the uncertainty-aware panoptic segmentation task, we address limitations with existing approaches by proposing new metrics that enable separate evaluation of spatial and semantic uncertainty. We additionally propose the use of the energy score, a proper scoring rule, for more robust evaluation of spatial output distributions. Using these metrics, we conduct an extensive evaluation of ProPanDL variants. Our results demonstrate that ProPanDL is capable of estimating well-calibrated and meaningful output distributions while still retaining strong performance on the base panoptic segmentation task.

Panoptic-Deeplab: A Simple, Strong, And Fast Baseline For Bottom-Up Panoptic Segmentation

Abstract: In this work, we introduce Panoptic-DeepLab, a simple, strong, and fast system for panoptic segmentation, aiming to establish a solid baseline for bottom-up methods that can achieve comparable performance of two-stage methods while yielding fast inference speed. In particular, PanopticDeepLab adopts the dual-ASPP and dual-decoder structures specific to semantic, and instance segmentation, respectively. The semantic segmentation branch is the same as the typical design of any semantic segmentation model (e.g., DeepLab), while the instance segmentation branch is class-agnostic, involving a simple instance center regression. As a result, our single Panoptic-DeepLab simultaneously ranks first at all three Cityscapes benchmarks, setting the new state-of-art of 84.2% mIoU, 39.0% AP, and 65.5% PQ on test set. Additionally, equipped with MobileNetV3, Panoptic-DeepLab runs nearly in real-time with a single 1025 x 2049 image (15.8 frames per second), while achieving a competitive performance on Cityscapes (54.1 PQ% on test set). On Mapillary Vistas test set, our ensemble of six models attains 42.7% PQ, outperforming the challenge winner in 2018 by a healthy margin of 1.5%. Finally, our Panoptic-DeepLab also performs on par with several topdown approaches on the challenging COCO dataset. For the first time, we demonstrate a bottom-up approach could deliver state-of-the-art results on panoptic segmentation.


In addition, all the experimental and profiling results are developed on PyTorch Geometric (PyG) framework #REFR , taking the average results of 10 runs. HGNAS settings.

For evaluating HGNAS, we consider three baselines: the popular point cloud processing model DGCNN #OTHEREFR , and two manually optimized methods on the DGCNN architecture #OTHEREFR .

Our experiments are conducted on the public benchmark Model-Net40 #OTHEREFR for classification task with 1024 points, and following the default hyperparameter settings in #OTHEREFR .

We assign 12 positions for the GNN supernet to cover DGCNN architectures.

During the design space exploration, the max iteration is set to 1000, while the population size of EA is set as 20.

Searching and training of HGNAS are both conducted on Nvidia V100 GPU.

During function search and operation search, the number of GNN supernet training epochs is set as 50 and 500, respectively.

Hardware-Aware Graph Neural Network Automated Design For Edge Computing Platforms

Abstract: Graph neural networks (GNNs) have emerged as a popular strategy for handling non-Euclidean data due to their state-of-the-art performance. However, most of the current GNN model designs mainly focus on task accuracy, lacking in considering hardware resources limitation and real-time requirements of edge application scenarios. Comprehensive profiling of typical GNN models indicates that their execution characteristics are significantly affected across different computing platforms, which demands hardware awareness for efficient GNN designs. In this work, HGNAS is proposed as the first Hardware-aware Graph Neural Architecture Search framework targeting resource constraint edge devices. By decoupling the GNN paradigm, HGNAS constructs a fine-grained design space and leverages an efficient multi-stage search strategy to explore optimal architectures within a few GPU hours. Moreover, HGNAS achieves hardware awareness during the GNN architecture design by leveraging a hardware performance predictor, which could balance the GNN model accuracy and efficiency corresponding to the characteristics of targeted devices. Experimental results show that HGNAS can achieve about 10.6× speedup and 88.2% peak memory reduction with a negligible accuracy loss compared to DGCNN on various edge devices, including Nvidia RTX3080, Jetson TX2, Intel i7-8700K and Raspberry Pi 3B+.

Fast Graph Representation Learning With Pytorch Geometric

Abstract: We introduce PyTorch Geometric, a library for deep learning on irregularly structured input data such as graphs, point clouds and manifolds, built upon PyTorch. In addition to general graph data structures and processing methods, it contains a variety of recently published methods from the domains of relational learning and 3D data processing. PyTorch Geometric achieves high data throughput by leveraging sparse GPU acceleration, by providing dedicated CUDA kernels and by introducing efficient mini-batch handling for input examples of different size. In this work, we present the library in detail and perform a comprehensive comparative study of the implemented methods in homogeneous evaluation scenarios.


Then, the vertex, whether or not it is active, processes its incoming properties, reduces them to a single new value, and applies this value to its property #REFR .

In this section, we demonstrate both the generality of TeAAL as a tool for modeling a broader class of accelerators and the value of TeAAL when proposing and evaluating new designs.

As an example, we model Graphicionado #OTHEREFR , which accelerates graph algorithms written in the vertexcentric programming paradigm, and use TeAAL to propose two optimizations.

Vertex-centric programming describes graph algorithms from the point of view of a single vertex.

During each iteration, a vertex, if active, sends its property to all of its destination vertices.

For some algorithms (e.g., PageRank #OTHEREFR ), all vertices are active every iteration, while for other algorithms (e.g., breadth-first search (BFS)), only the vertices whose properties are updated are active in the next iteration #OTHEREFR .

In this evaluation, we will focus on the latter, more complex, class of algorithms.

Figure 11a shows a cascade of Einsums representing Graphicionado #OTHEREFR .

We omit the rest of the TeAAL specification for space. Graphicionado divides its evaluation into two stages.

During the processing phase, the active vertices A0 are used to select the edges that need to be processed SO (Line 2), the weights of those edges are combined with the source vertex properties (Line 3), and reduced into R (implicit in Line 3).

Teaal: A Declarative Framework For Modeling Sparse Tensor Accelerators

Abstract: Over the past few years, the explosion in sparse tensor algebra workloads has led to a corresponding rise in domain-specific accelerators to service them. Due to the irregularity present in sparse tensors, these accelerators employ a wide variety of novel solutions to achieve good performance. At the same time, prior work on design-flexible sparse accelerator modeling does not express this full range of design features, making it difficult to understand the impact of each design choice and compare or extend the state-of-the-art. To address this, we propose TeAAL: a language and compiler for the concise and precise specification and evaluation of sparse tensor algebra architectures. We use TeAAL to represent and evaluate four disparate state-ofthe-art accelerators-ExTensor, Gamma, OuterSPACE, and SIGMA-and verify that it reproduces their performance with high accuracy. Finally, we demonstrate the potential of TeAAL as a tool for designing new accelerators by showing how it can be used to speed up Graphicionado-by 38× on BFS and 4.3× on SSSP.

Graphmat: High Performance Graph Analytics Made Productive

Abstract: Given the growing importance of large-scale graph analytics, there is a need to improve the performance of graph analysis frameworks without compromising on productivity. GraphMat is our solution to bridge this gap between a user-friendly graph analytics framework and native, hand-optimized code. GraphMat functions by taking vertex programs and mapping them to high performance sparse matrix operations in the backend. We thus get the productivity benefits of a vertex programming framework without sacrificing performance. GraphMat is a single-node multicore graph framework written in C++ which has enabled us to write a diverse set of graph algorithms with the same effort compared to other vertex programming frameworks. GraphMat performs 1.1-7X faster than high performance frameworks such as GraphLab, CombBLAS and Galois. GraphMat also matches the performance of MapGraph, a GPU-based graph framework, despite running on a CPU platform with significantly lower compute and bandwidth resources. It achieves better multicore scalability (13-15X on 24 cores) than other frameworks and is 1.2X off native, hand-optimized code on a variety of graph algorithms. Since GraphMat performance depends mainly on a few scalable and well-understood sparse matrix operations, GraphMat can naturally benefit from the trend of increasing parallelism in future hardware.


Relationship Between Kl Divergence And Fisher Divergence

See Appendix D for a derivation and a comparison with a similar result by #REFR .

Interestingly, if we make the assumption that the score network encodes a conservative vector field, then the time derivative of the KL divergence equals a scaled Fisher divergence:

Understanding The Diffusion Objective As A Weighted Integral Of Elbos

Abstract: Diffusion models in the literature are optimized with various objectives that are special cases of a weighted loss, where the weighting function specifies the weight per noise level. Uniform weighting corresponds to maximizing the ELBO, a principled approximation of maximum likelihood. In current practice diffusion models are optimized with non-uniform weighting due to better results in terms of sample quality. In this work we expose a direct relationship between the weighted loss (with any weighting) and the ELBO objective. We show that the weighted loss can be written as a weighted integral of ELBOs, with one ELBO per noise level. If the weighting function is monotonic, then the weighted loss is a likelihood-based objective: it maximizes the ELBO under simple data augmentation, namely Gaussian noise perturbation. Our main contribution is a deeper theoretical understanding of the diffusion objective, but we also performed some experiments comparing monotonic with non-monotonic weightings, finding that monotonic weighting performs competitively with the best published results.

Interpretation And Generalization Of Score Matching

Abstract: Score matching is a recently developed parameter learning method that is particularly effective to complicated high dimensional density models with intractable partition functions. In this paper, we study two issues that have not been completely resolved for score matching. First, we provide a formal link between maximum likelihood and score matching. Our analysis shows that score matching finds model parameters that are more robust with noisy training data. Second, we develop a generalization of score matching. Based on this generalization, we further demonstrate an extension of score matching to models of discrete data.


Editing LLMs In the past, LLMs have been predominantly adapted to new tasks and knowledge through finetuning #REFR .

Probing representations of individual situations Neural LMs have also been shown to build representations of context-dependent knowledge.

show that they track aspects of entity state over a discourse, and this state can be extracted from LM representations of contextualized entity tokens.

Furthermore, many LMs have been (indirectly) evaluated on their ability to track context-dependent knowledge by having their performance measured on downstream reading comprehension tasks in wich the LM is expected to answer questions about facts within a discourse.

Reading comprehension datasets such as CoQA #OTHEREFR , RACE #OTHEREFR , and SQuAD #OTHEREFR are now part of the standard evaluation suite for new LMs; and most modern LMs perform well #OTHEREFR . However, generating does not always imply knowing.

Datasets contain spurious correlations #OTHEREFR , and LMs are sensitive to the phrasing of prompts and questions #OTHEREFR .

Recently, with very large LMs, new classes of adaptation methods have been introduced, which generally fall into one of the following two categories:

(1) Prompt design approaches prepend a textual prompt to each example specifying the adaptation target #OTHEREFR .

Though these techniques have generally been explored in the context of teaching LMs new tasks, they have also been used to imbue LMs with new knowledge.

(2) Prefix-tuning approaches prepend continuous learned tokens ahead of each example.

The learned tokens specify a task for the LM similar to how a textual prompt would #OTHEREFR .

Measuring And Manipulating Knowledge Representations In Language Models

Abstract: Neural language models (LMs) represent facts about the world described by text. Sometimes these facts derive from training data (in most LMs, a representation of the word banana encodes the fact that bananas are fruits). Sometimes facts derive from input text itself (a representation of the sentence I poured out the bottle encodes the fact that the bottle became empty). Tools for inspecting and modifying LM fact representations would be useful almost everywhere LMs are used: making it possible to update them when the world changes, to localize and remove sources of bias, and to identify errors in generated text. We describe REMEDI, an approach for querying and modifying factual knowledge in LMs. REMEDI learns a map from textual queries to fact encodings in an LM's internal representation system. These encodings can be used as knowledge editors: by adding them to LM hidden representations, we can modify downstream generation to be consistent with new facts. REMEDI encodings can also be used as model probes: by comparing them to LM representations, we can ascertain what properties LMs attribute to mentioned entities, and predict when they will generate outputs that conflict with background knowledge or input text. REMEDI thus links work on probing, prompting, and model editing, and offers steps toward general tools for fine-grained inspection and control of knowledge in LMs.


Our IMP obtains similar performance to Super-Glue* #REFR and outperforms SGMNet and ClusterGNN especially for night images.

As most filtering methods don't provide results on visual localization task, we only show results of OANet #OTHEREFR and AdaLAM #OTHEREFR .

OANet and AdaLAM give close accuracy to MNN at error thresholds of 0.25m, 2 • and 0.5m, 5 • , but much better performance at threshold of 5m, 10 • , because Table 3 .

Results on Aachen v1.0 (top) and v1.1 (bottom) dataset #OTHEREFR . The best and second best results are highlighted.

test images with larger viewpoint changes are more sensitive to outliers which can be partially filtered by OANet and AdaLAM.

Note that our models are only trained on the MegaDepth dataset #OTHEREFR while SuperGlue* is additionally pretrained on Oxford and Pairs dataset #OTHEREFR . EIMP slightly outperforms IMP and SuperGlue*.

That is because in the long-term large-scale localization task, query and reference images usually have larger viewpoint and illumination changes with more keypoints without true matches.

EIMP effectively discards these keypoints, guaranteeing the quality of matches and thus improving the localization accuracy.

Imp: Iterative Matching And Pose Estimation With Adaptive Pooling

Abstract: Previous methods solve feature matching and pose estimation using a two-stage process by first finding matches and then estimating the pose. As they ignore the geometric relationships between the two tasks, they focus on either improving the quality of matches or filtering potential outliers, leading to limited efficiency or accuracy. In contrast, we propose an iterative matching and pose estimation framework (IMP) leveraging the geometric connections between the two tasks: a few good matches are enough for a roughly accurate pose estimation; a roughly accurate pose can be used to guide the matching by providing geometric constraints. To this end, we implement a geometry-aware recurrent attention-based module which jointly outputs sparse matches and camera poses. Specifically, for each iteration, we first implicitly embed geometric information into the module via a pose-consistency loss, allowing it to predict geometry-aware matches progressively. Second, we introduce an efficient IMP, called EIMP, to dynamically discard keypoints without potential matches, avoiding redundant updating and significantly reducing the quadratic time complexity of attention computation in transformers. Experiments on YFCC100m, Scannet, and Aachen Day-Night datasets demonstrate that the proposed method outperforms previous approaches in terms of accuracy and efficiency.

Superglue: Learning Feature Matching With Graph Neural Networks

Abstract: This paper introduces SuperGlue, a neural network that matches two sets of local features by jointly finding correspondences and rejecting non-matchable points. Assignments are estimated by solving a differentiable optimal transport problem, whose costs are predicted by a graph neural network. We introduce a flexible context aggregation mechanism based on attention, enabling SuperGlue to reason about the underlying 3D scene and feature assignments jointly. Compared to traditional, hand-designed heuristics, our technique learns priors over geometric transformations and regularities of the 3D world through end-to-end training from image pairs. SuperGlue outperforms other learned approaches and achieves state-of-the-art results on the task of pose estimation in challenging real-world indoor and outdoor environments. The proposed method performs matching in real-time on a modern GPU and can be readily integrated into modern SfM or SLAM systems. The code and trained weights are publicly available at https://github.com/magicleap/SuperGluePretrainedNetwork.


Vision-Based Trajectory Analysis In Sports

In #REFR , Mixed Integer Programming was used to track, under motion blur conditions, the position of volleyball, basketball, and soccer balls, with the goal of computing their motion trajectories.

Computer vision-based techniques have been successfully used in different sports disciplines to reconstruct the trajectory of various elements #OTHEREFR . Chen et al.

developed an algorithm to compute the 3D trajectory of a volleyball ball in a static-camera video by transforming the output of the ball's visual tracker with prior knowledge of the volleyball court #OTHEREFR . In similar capturing settings, Calandre et al.

reconstructed the trajectory of table-tennis  balls with a visual detection and tracking module, a registration between image and table coordinates , and a kinematic ball motion model #OTHEREFR .

In #OTHEREFR , the tracking-by-deblatting paradigm was introduced for better localize of -hence compute better trajectories -fast-moving objects such as tabletennis or tennis balls in static camera videos.

In basketball analytics, different solutions have been proposed to reconstruct the trajectories of moving players #OTHEREFR .

All these research endeavors presented a pipeline composed of a visual tracking algorithm to get player-specific localizations across video frames, and the application of a homography transformation between frame and court coordinates.

Their differences lie in the tracking principles used, such as histogram-based condensation #OTHEREFR , Cam-Shift #OTHEREFR , deformable part models and kalman filters #OTHEREFR , or deep learning methods #OTHEREFR , and in the homography estimation, based either on manual annotation #OTHEREFR or on the matching of court lines and boundaries #OTHEREFR .

Similar approaches have been also exploited in computer visionbased applications for soccer #OTHEREFR and ice-hockey #OTHEREFR .

The methodology described in this paper is similar to #OTHEREFR in the design of a system to track an athlete (in our case a skier), and to compute key-point correspondence from which to estimate a homography.

Visualizing Skiers' Trajectories In Monocular Videos

Abstract: Figure 1. Visualization of skiers' trajectories in monocular videos. We present SkiTraVis, an algorithm to compute and visualize the trajectory performed by an alpine skier in videos acquired by a single monocular camera. SkiTraVis takes in input a streaming video capturing a skier and visualizes its past trajectory in each frame. In this picture, the first and third rows show the input frames, while the second and fourth the trajectory computed by SkiTraVis. The interval in seconds from the start of the video is also reported.

What Players Do With The Ball: A Physically Constrained Interaction Modeling

Abstract: Tracking the ball is critical for video-based analysis of team sports. However, it is difficult, especially in lowresolution images, due to the small size of the ball, its speed that creates motion blur, and its often being occluded by players. In this paper, we propose a generic and principled approach to modeling the interaction between the ball and the players while also imposing appropriate physical constraints on the ball's trajectory. We show that our approach, formulated in terms of a Mixed Integer Program, is more robust and more accurate than several state-of-the-art approaches on real-life volleyball, basketball, and soccer sequences.


The experimental results support our idea that the new algorithm is more robust to noise and point discrepancy in point clouds than #REFR .

This paper is especially related to #OTHEREFR which proposed a two-stage procedure to recover correspondences between unlabelled, affinely transformed point sets.

The first step finds the affine transformation and the second the matching between the point sets.

Specifically, we adopt their first step of using the Grassmannian which is invariant under linear (affine) transformations of the point sets.

As noted in #OTHEREFR it is provably better, in certain cases, to use an indefinite relaxation of the Quadratic Assignment Problem (QAP), used to find the matching, which almost always discovers the optimal permutation, while the common convex relaxation almost always fails.

These theoretical results bring us to propose a relaxed quadratic programming approach to find the correct permutation (match) between the point sets.

Robust Affine Feature Matching Via Quadratic Assignment On Grassmannians

Abstract: GraNNI (Grassmannians for Nearest Neighbours Identification) a new algorithm to solve the problem of affine registration is proposed. The algorithm is based on the Grassmannian of k-dimensional planes in R n and minimizing the Frobenius norm between the two elements of the Grassmannian. The Quadratic Assignment Problem (QAP) is used to find the matching. The results of the experiments show that the algorithm is more robust to noise and point discrepancy in point clouds than previous approaches.

A Grassmannian Graph Approach To Affine Invariant Feature Matching

Abstract: In this work, we present a novel and practical approach to address one of the longstanding problems in computer vision: 2D and 3D affine invariant feature matching. Our Grassmannian Graph (GrassGraph) framework employs a two stage procedure that is capable of robustly recovering correspondences between two unorganized, affinely related feature (point) sets. The first stage maps the feature sets to an affine invariant Grassmannian representation, where the features are mapped into the same subspace. It turns out that coordinate representations extracted from the Grassmannian differ by an arbitrary orthonormal matrix. In the second stage, by approximating the Laplace-Beltrami operator (LBO) on these coordinates, this extra orthonormal factor is nullified, providing true affine-invariant coordinates which we then utilize to recover correspondences via simple nearest neighbor relations. The resulting GrassGraph algorithm is empirically shown to work well in non-ideal scenarios with noise, outliers, and occlusions. Our validation benchmarks use an unprecedented 440,000+ experimental trials performed on 2D and 3D datasets, with a variety of parameter settings and competing methods. State-of-the-art performance in the majority of these extensive evaluations confirm the utility of our method.


Interestingly, statistical beamforming is shown to be very effective in the case of sparse channels #REFR , which supports the observed behavior here.

We observe that our proposed DNN-based technique outperforms all rival methods (except ZF with perfect CSI).

In particular, we see a significant performance advantage in comparison to the DNN-based method in #OTHEREFR , especially for small feedback sizes.

This should be mainly attributed to the fact that our #OTHEREFR ACS method in #OTHEREFR MRT with perfect CSI ZF with perfect CSI Significant Sum-Rate gain for small B #OTHEREFR ACS method in #OTHEREFR MRT with perfect CSI Gain due to sum-rate maximization Gain due to pre-beamforming proposed scheme exploits channel statistics knowledge at the BS.

Even when there is practically no feedback (B Ñ 0), our scheme is able to achieve a relatively large sum-rate because one component of the designed precoder in #OTHEREFR , namely the pre-beamforming matrix B depends only on channel statistics and not the feedback.

In this case, our DNN is essentially performing a kind of statistical beamforming with (almost) no CSI.

The proposed method also outperforms MRT, which is due to the use of ZF precoding in our architecture.

The advantage with respect to the case with no pre-beamforming matrix (the red curve) is rather small.

This is due to the fact that the channels are sparse and L ă β, and therefore with sufficient feedback no beam-selection is necessary.

Here, the optimized beam-selection vector is λ « 1, which is very close to no pre-beamforming with B " F H .

Finally, we see a similar advantage in comparison to the ACS method in #OTHEREFR , resulting from the direct maximization of sumrate rather than multiplexing gain.

Deep-Learning Aided Channel Training And Precoding In Fdd Massive Mimo With Channel Statistics Knowledge

Abstract: We propose a method for channel training and precoding in FDD massive MIMO based on deep neural networks (DNNs), exploiting Downlink (DL) channel covariance knowledge. The DNN is optimized to maximize the DL multiuser sum-rate, by producing a pre-beamforming matrix based on user channel covariances that maps the original channel vectors to "effective channels". Measurements of these effective channels are received at the users via common pilot transmission and sent back to the base station (BS) through analog feedback without further processing. The BS estimates the effective channels from received feedback and constructs a linear precoder by concatenating the optimized pre-beamforming matrix with a zero-forcing precoder over the effective channels. We show that the proposed method yields significantly higher sum-rates than the state-of-the-art DNN-based channel training and precoding scheme, especially in scenarios with small pilot and feedback size relative to the channel coherence block length. Unlike many works in the literature, our proposition does not involve deployment of a DNN at the user side, which typically comes at a high computational cost and parameter-transmission overhead on the system, and is therefore considerably more practical. Index Terms-FDD massive MIMO, channel statistics knowledge, analog feedback, DNN-based training and precoding.

Statistical Beamforming For Fdd Downlink Massive Mimo Via Spatial Information Extraction And Beam Selection

Abstract: In this paper, we study the beamforming design problem in frequency-division duplexing (FDD) downlink massive MIMO systems, where instantaneous channel state information (CSI) is assumed to be unavailable at the base station (BS). We propose to extract the information of the angle-of-departures (AoDs) and the corresponding large-scale fading coefficients (a.k.a. spatial information) of the downlink channel from the uplink channel estimation procedure, based on which a novel downlink beamforming design is presented. By separating the subpaths for different users based on the spatial information and the hidden sparsity of the physical channel, we construct near-orthogonal virtual channels in the beamforming design. Furthermore, we derive a sum-rate expression and its approximations for the proposed system. Based on these closed-form rate expressions, we develop two low-complexity beam selection schemes and carry out asymptotic analysis to provide valuable insights on the system design. Numerical results demonstrate a significant performance improvement of our proposed algorithm over the state-of-the-art beamforming approach.


Linear-Size Additive Spanners In Sub-Quadratic Time

In this subsection, we prove the following theorem, which shows that an additive spanner with the current best error (as in #REFR ) can be computed in subquadratic time.

In this section we provide the proof of Theorem 1.2.

We will first show in Section 6.1 a simple subquadratic time algorithm for computing an +O(n 3/7+ε )-additive spanner.

Then we will slightly modify it to achieve better additive error bound in Section 6.2.

6.1 A subquadratic time algorithm for O(n 3/7+ε )-additive spanner

There is an algorithm, that, given any undirected unweighted graph on n vertices and m edges, and any parameter ε > 0, computes an spanner on 2 O(1/ε) • n edges with +Õ(n 3/7+ε ) error, in timeÕ m + 2 O(1/ε) • n 13/7 .

We start with the following lemma for a preliminary sparsification of the input graph.

There is an algorithm, that given any graph G and parameter 1 < d < n, computes inÕ(m) time an +Õ(n/d) additive spanner G ′ of G with |E(G ′ )| ≤ O(nd) edges.

Almost-Optimal Sublinear Additive Spanners

Abstract: Given an undirected unweighted graph G = (V, E) on n vertices and m edges, a subgraph H ⊆ G is a spanner of G with stretch function f : R + → R + , iff for every pair s, t of vertices in V , dist H (s, t) ≤ f (dist G (s, t)). When f (d) = d + o(d), H is called a sublinear additive spanner ; when f (d) = d + o(n), H is called an additive spanner, and f (d) − d is usually called the additive stretch of H. As our primary result, we show that for any constant δ > 0 and constant integer k ≥ 2, every graph on n vertices has a sublinear additive spanner with stretch function f (d) = d + O(d 1−1/k) and O n 1+ 1+δ 2 k+1 −1 edges. When k = 2, this improves upon the previous spanner construction with stretch function f (d) = d + O(d 1/2) andÕ(n 1+3/17) edges [Chechik, 2013]; for any constant integer k ≥ 3, this improves upon the previous spanner construction with stretch function f (d) = d + O(d 1−1/k) and O n

Better Distance Preservers And Additive Spanners

Abstract: We make improvements to the upper bounds on several popular types of distance preserving graph sketches. These sketches are all various restrictions of the {\em additive pairwise spanner} problem, in which one is given an undirected unweighted graph $G$, a set of node pairs $P$, and an error allowance $+\beta$, and one must construct a sparse subgraph $H$ satisfying $\delta_H(u, v) \le \delta_G(u, v) + \beta$ for all $(u, v) \in P$. The first part of our paper concerns {\em pairwise distance preservers}, which make the restriction $\beta=0$ (i.e. distances must be preserved {\em exactly}). Our main result here is an upper bound of $|H| = O(n^{2/3}|P|^{2/3} + n|P|^{1/3})$ when $G$ is undirected and unweighted. This improves on existing bounds whenever $|P| = \omega(n^{3/4})$, and it is the first such improvement in the last ten years. We then devise a new application of distance preservers to graph clustering algorithms, and we apply this algorithm to {\em subset spanners}, which require $P = S \times S$ for some node subset $S$, and {\em (standard) spanners}, which require $P = V \times V$. For both of these objects, our construction generalizes the best known bounds when the error allowance is constant, and we obtain the strongest polynomial error/sparsity tradeoff that has yet been reported (in fact, for subset spanners, ours is the {\em first} nontrivial construction that enjoys improved sparsity from a polynomial error allowance). We leave open a conjecture that $O(n^{2/3}|P|^{2/3} + n)$ pairwise distance preservers are possible for undirected unweighted graphs. Resolving this conjecture in the affirmative would improve and simplify our upper bounds for all the graph sketches mentioned above.


The learning rate is initialized to 1.0 for GLM and 0.01 for SLM, decreased at each iteration with polynomial decay #REFR , and further decreased by 1/10 at the ResNet50 backbone.

We use an ImageNet #OTHEREFR pretrained ResNet50 #OTHEREFR as the CAM backbone in GSLM and the classification layer is a 1 × 1 convolution with output channels adapted to the number of categories in the dataset.

In Seed Reactivation, we adopt ψ k for activation values limitation as in Eq. 4, where k is set to 6. The balance factor α in Eq. (6) is set to 0.5.

The thresholds θ f g and θ bg in Coarse Generation are set to 0.30 and 0.05, respectively.

The number of iterations of SLM is set to 3 for GSLM.

We adopt stochastic gradient descent (SGD) for optimization, with different optimization settings for GLM and SLM.

The batch size is 16, the number of training epochs is 5, and the weight decay is 0.001.

The remaining optimization settings are the same as in #OTHEREFR .

Moreover, we build a stronger baseline GSLM + by replacing GLM in GSLM with IRN #OTHEREFR and keeping SLM the same but with a single iteration. All other settings are the same as GSLM.

Exploit Cam By Itself: Complementary Learning System For Weakly Supervised Semantic Segmentation

Abstract: Weakly Supervised Semantic Segmentation (WSSS) with image-level labels has long been suffering from fragmentary object regions led by Class Activation Map (CAM), which is incapable of generating fine-grained masks for semantic segmentation. To guide CAM to find more non-discriminating object patterns, this paper turns to an interesting working mechanism in agent learning named Complementary Learning System (CLS). CLS holds that the neocortex builds a sensation of general knowledge, while the hippocampus specially learns specific details, completing the learned patterns. Motivated by this simple but effective learning pattern, we propose a General-Specific Learning Mechanism (GSLM) to explicitly drive a coarse-grained CAM to a fine-grained pseudo mask. Specifically, GSLM develops a General Learning Module (GLM) and a Specific Learning Module (SLM). The GLM is trained with image-level supervision to extract coarse and general localization representations from CAM. Based on the general knowledge in the GLM, the SLM progressively exploits the specific spatial knowledge from the localization representations, expanding the CAM in an explicit way. To this end, we propose the Seed Reactivation to help SLM reactivate nondiscriminating regions by setting a boundary for activation values, which successively identifies more regions of CAM. Without extra refinement processes, our method is able to achieve breakthrough improvements for CAM of over 20.0% mIoU on PASCAL VOC 2012 and 10.0% mIoU on MS COCO 2014 datasets, representing a new state-of-the-art among existing WSSS methods.

Parsenet: Looking Wider To See Better

Abstract: We present a technique for adding global context to deep convolutional networks for semantic segmentation. The approach is simple, using the average feature for a layer to augment the features at each location. In addition, we study several idiosyncrasies of training, significantly increasing the performance of baseline networks (e.g. from FCN). When we add our proposed global feature, and a technique for learning normalization parameters, accuracy increases consistently even over our improved versions of the baselines. Our proposed approach, ParseNet, achieves state-of-the-art performance on SiftFlow and PASCAL-Context with small additional computational cost over baselines, and near current state-of-the-art performance on PASCAL VOC 2012 semantic segmentation with a simple approach. Code is available at this https URL .


For the yaw rate estimation, the errors of the presented state of the art approaches are similar except for the GRU-based #REFR approach that performs the worst.

The acceleration plot shows that the maximum acceleration is a max y = 2.10 m/s 2 indicating low dynamic driving.

The MAE of the different observers for the considered maneuver are shown in Table III .

The proposed approach is able to perform best among the considered state of the art observers for the three variables.

It can be seen that for the longitudinal velocity estimation, the KalmanNet based approach #OTHEREFR ranks second with an estimation error that is close to our approach; the EKF based on the dynamic bicycle model #OTHEREFR shows the worst performance with an error 3 times higher than the proposed approach.

For the lateral velocity estimation, the GRU-based approach #OTHEREFR ranks second; the KalmanNet based approach #OTHEREFR performs the worst with an error 5 times higher than the proposed approach.

To further inspect the presented results, we plot the estimations by our proposed approach and the next best performing approach of each of the variables.

We plot them in a subset of the maneuver sequence summarized in Fig. 6 .

This subset includes the highest lateral accelerations in the dataset.

All approaches are compared to the iTraceRT sensor values for reference.

Higher accelerations are associated with more dynamic maneuvers and thus more challenging estimations. The plot is shown in Fig. 7 .

Robust Lstm-Based Vehicle Velocity Observer For Regular And Near-Limits Applications

Abstract: Accurate velocity estimation is key to vehicle control. While the literature describes how model-based and learningbased observers are able to estimate a vehicle's velocity in normal driving conditions, the challenge remains to estimate the velocity in near-limits maneuvers while using only conventional in-car sensors. In this paper, we introduce a novel neural network architecture based on Long Short-Term Memory (LSTM) networks to accurately estimate the vehicle's velocity in different driving conditions, including maneuvers at the limits of handling. The approach has been tested on real vehicle data and it provides more accurate estimations than state-of-the-art model-based and learning-based methods, for both regular and near-limits driving scenarios. Our approach is robust since the performance of the state-of-the-art observers deteriorates with higher dynamics, while our method adapts to different maneuvers, providing accurate estimations even at the vehicle's limits of handling.

End-To-End Velocity Estimation For Autonomous Racing

Abstract: Velocity estimation plays a central role in driverless vehicles, but standard and affordable methods struggle to cope with extreme scenarios like aggressive maneuvers due to the presence of high sideslip. To solve this, autonomous race cars are usually equipped with expensive external velocity sensors. In this paper, we present an end-to-end recurrent neural network that takes available raw sensors as input (IMU, wheel odometry, and motor currents) and outputs velocity estimates. The results are compared to two state-of-the-art Kalman filters, which respectively include and exclude expensive velocity sensors. All methods have been extensively tested on a formula student driverless race car with very high sideslip (10{\deg} at the rear axle) and slip ratio (~20%), operating close to the limits of handling. The proposed network is able to estimate lateral velocity up to 15x better than the Kalman filter with the equivalent sensor input and matches (0.06 m/s RMSE) the Kalman filter with the expensive velocity sensor setup.


Specifically, W (v) and V (v) are iteratively updated based on gradient descent method, it is necessary to prove that their corresponding minimization functions Eq. #REFR and Eq.

Herein, we analyze the convergence property for the proposed HLRCF.

(29) will gradually decrease and eventually stabalize as the number of iterations increases.

First, we introduce an auxiliary function Z(•), whose definition is given as Definition 1. The complete proof is provided in the Appendix A. Briefly, we can derive the following inequality chain

The above inequalities indicate that the value of the objective function decreases with each iteration, thereby ensuring that the optimization of both W (v) and V (v) occurs in the direction of convergence.

Hyper-Laplacian Regularized Concept Factorization In Low-Rank Tensor Space For Multi-View Clustering

Abstract: Tensor-oriented multi-view subspace clustering has achieved significant strides in assessing highorder correlations and improving clustering analysis of multi-view data. Nevertheless, most of existing investigations are typically hampered by the two flaws. First, self-representation based tensor subspace learning usually induces high time and space complexity, and is limited in perceiving nonlinear local structure in the embedding space. Second, the tensor singular value decomposition (t-SVD) model redistributes each singular value equally without considering the diverse importance among them. To well cope with the issues, we propose a hyper-Laplacian regularized concept factorization (HLRCF) in low-rank tensor space for multi-view clustering. Specifically, we adopt the concept factorization to explore the latent cluster-wise representation of each view. Further, the hypergraph Laplacian regularization endows the model with the capability of extracting the nonlinear local structures in the latent space. Considering that different tensor singular values associate structural information with unequal importance, we develop a self-weighted tensor Schatten p-norm to constrain the tensor comprised of all cluster-wise representations. Notably, the tensor with smaller size greatly decreases the time and space complexity in the low-rank optimization. Finally, experimental results on eight benchmark datasets exhibit that HLRCF outperforms other multi-view methods, showingcasing its superior performance.

Nuclear Norm Of Higher-Order Tensors

Abstract: We establish several mathematical and computational properties of the nuclear norm for higher-order tensors. We show that like tensor rank, tensor nuclear norm is dependent on the choice of base field --- the value of the nuclear norm of a real 3-tensor depends on whether we regard it as a real 3-tensor or a complex 3-tensor with real entries. We show that every tensor has a nuclear norm attaining decomposition and every symmetric tensor has a symmetric nuclear norm attaining decomposition. There is a corresponding notion of nuclear rank that, unlike tensor rank, is upper semicontinuous. We establish an analogue of Banach's theorem for tensor spectral norm and Comon's conjecture for tensor rank --- for a symmetric tensor, its symmetric nuclear norm always equals its nuclear norm. We show that computing tensor nuclear norm is NP-hard in several sense. Deciding weak membership in the nuclear norm unit ball of 3-tensors is NP-hard, as is finding an $\varepsilon$-approximation of nuclear norm for 3-tensors. In addition, the problem of computing spectral or nuclear norm of a 4-tensor is NP-hard, even if we restrict the 4-tensor to be bi-Hermitian, bisymmetric, positive semidefinite, nonnegative valued, or all of the above. We discuss some simple polynomial-time approximation bounds. As an aside, we show that the nuclear $(p,q)$-norm of a matrix is NP-hard in general but can be computed in polynomial-time if $p=1$, $q = 1$, or $p=q=2$, with closed-form expressions for the nuclear $(1,q)$- and $(p,1)$-norms.


These two results make up one ( 3 2 + )-approximation that improves on the best known result by Jansen and Land #REFR in multiple ways.

The resulting knapsack instance will only have a small amount of different item sizes.

We then apply an algorithm introduced by Axiotis and Tzamos #OTHEREFR that works well on such instances.

Thanks to our rounding we will be able to do the pre-processing of their algorithm in linear time as well. Theorem 2.

For moldable job scheduling there exists an algorithm that for instances with m ≤ 16n and for any > 0 yields a #OTHEREFR 2 + approximation in time:

O(n log 2 ( 1 + log( m) ) + n log( 1 )log( m))

For large m we manage to reduce the dependency on m even further.

When m is small we improve on their running time by reducing the dependency on by a factor of 1 and several polylogarithmic factors.

We also argue that our algorithm is overall simpler compared to theirs, as we do not require to solve knapsack with compressible items in a complicated manner.

Instead our algorithm merely constructs the modified knapsack instance and delegates to a simple and elegant algorithm from Axiotis and Tzamos #OTHEREFR .

Improved Algorithms For Monotone Moldable Job Scheduling Using Compression And Convolution

Abstract: In the moldable job scheduling problem one has to assign a set of n jobs to m machines, in order to minimize the time it takes to process all jobs. Each job is moldable, so it can be assigned not only to one but any number of the equal machines. We assume that the work of each job is monotone and that jobs can be placed noncontiguously. In this work we present a (3 2 +)-approximation algorithm with a worst-case runtime of O(n log 2 (1 + log(m)) + n log(1)log(m)) when m ≤ 16n. This is an improvement over the best known algorithm of the same quality by a factor of 1 and several logarithmic dependencies. We complement this result with an improved FPTAS with running time O(n log 2 (1 + log(m))) for instances with many machines m > 8 n. This yields a 3 2-approximation with runtime O(n log 2 (log m)) when m > 16n. We achieve these results through one new core observation: In an approximation setting one does not need to consider all m possible allotments for each job. We will show that we can reduce the number of relevant allotments for each job from m to O(1 + log(m)). Using this observation immediately yields the improved FPTAS. For the other result we use a reduction to the knapsack problem first introduced by Mounié, Rapine and Trystram. We use the reduced number of machines to give a new elaborate rounding scheme and define a modified version of this this knapsack instance. This in turn allows for the application of a convolution based algorithm by Axiotis and Tzamos. We further back our theoretical results through a practical implementation and compare our algorithm to the previously known best result.

Scheduling Monotone Moldable Jobs In Linear Time

Abstract: A moldable job is a job that can be executed on an arbitrary number of processors, and whose processing time depends on the number of processors allotted to it. A moldable job is monotone if its work doesn't decrease for an increasing number of allotted processors. We consider the problem of scheduling monotone moldable jobs to minimize the makespan. We argue that for certain compact input encodings a polynomial algorithm has a running time polynomial in n and log(m), where n is the number of jobs and m is the number of machines. We describe how monotony of jobs can be used to counteract the increased problem complexity that arises from compact encodings, and give tight bounds on the approximability of the problem with compact encoding: it is NP-hard to solve optimally, but admits a PTAS. The main focus of this work are efficient approximation algorithms. We describe different techniques to exploit the monotony of the jobs for better running times, and present a (3/2+{\epsilon})-approximate algorithm whose running time is polynomial in log(m) and 1/{\epsilon}, and only linear in the number n of jobs.


Instead of assuming linear rewards, GAIL #REFR leverages DNNs to represent the reward function (also called the discriminator) and shows dramatic performance improvements.

A basic difference between BC and DAGGER is that the former learns with only expert data, while the latter needs expert policies and on-line interactions.

IRL deals with the IL problem from the aspect of RL #OTHEREFR .

There are two alternate steps in IRL: (1) learn a reward function that can assign high rewards to expert data and low rewards to agent's experiences; (2) conduct RL with the learned reward function in step #OTHEREFR to improve the performance of agents.

Abbeel and Ng #OTHEREFR present apprenticeship learning, which employs linear functions to approximate the reward function.

Maximum entropy IRL #OTHEREFR uses probabilistic models to obtain a reward function with less ambiguity based on maximum entropy and maintains performance guarantees.

For more recent advances in IRL, please refer to #OTHEREFR - #OTHEREFR .

Quantum Imitation Learning

Abstract: Despite remarkable successes in solving various complex decision-making tasks, training an imitation learning (IL) algorithm with deep neural networks (DNNs) suffers from the high computation burden. In this work, we propose quantum imitation learning (QIL) with a hope to utilize quantum advantage to speed up IL. Concretely, we develop two QIL algorithms, quantum behavioural cloning (Q-BC) and quantum generative adversarial imitation learning (Q-GAIL). Q-BC is trained with a negative log-likelihood loss in an off-line manner that suits extensive expert data cases, whereas Q-GAIL works in an inverse reinforcement learning scheme, which is on-line and on-policy that is suitable for limited expert data cases. For both QIL algorithms, we adopt variational quantum circuits (VQCs) in place of DNNs for representing policies, which are modified with data re-uploading and scaling parameters to enhance the expressivity. We first encode classical data into quantum states as inputs, then perform VQCs, and finally measure quantum outputs to obtain control signals of agents. Experiment results demonstrate that both Q-BC and Q-GAIL can achieve comparable performance compared to classical counterparts, with the potential of quantum speed-up. To our knowledge, we are the first to propose the concept of QIL and conduct pilot studies, which paves the way for the quantum era. Index Terms-Quantum imitation learning (QIL), variational quantum circuits (VQCs), imitation learning (IL), behavioural cloning (BC), inverse reinforcement learning (IRL).

Generative Adversarial Imitation Learning

Abstract: Consider learning a policy from example expert behavior, without interaction with the expert or access to reinforcement signal. One approach is to recover the expert's cost function with inverse reinforcement learning, then extract a policy from that cost function with reinforcement learning. This approach is indirect and can be slow. We propose a new general framework for directly extracting a policy from data, as if it were obtained by reinforcement learning following inverse reinforcement learning. We show that a certain instantiation of our framework draws an analogy between imitation learning and generative adversarial networks, from which we derive a model-free imitation learning algorithm that obtains significant performance gains over existing model-free methods in imitating complex behaviors in large, high-dimensional environments.


For privacy guarantee, we construct a simple yet general membership inference attack (MIA) based on #REFR using the output of the unlearned model and test the attack success rate (ASR).

For the fine-tune process in Boundary Unlearning we use a learning rate of 10 −5 for 10 epochs.

Note that we use the same forgetting class as the forgetting data for all unlearning methods and the rest of classes are the remaining data. Metrics.

We verify the unlearning performance on both utility and privacy guarantees.

For utility guarantee, we utilize four accuracy metrics: accuracy on the remaining training data D r , forgetting training data D f , remaining testing data D rt , forgetting testing data D f t .

The unlearned model is expected to get close accuracy with the retrained model.

MIA aims to infer whether a data record was used to train a target ML model or not #OTHEREFR , and its ASR is widely used as an evaluation metric to verify the privacy guarantee of an unlearning method #OTHEREFR .

Ideally, a forgetting procedure should have the same attack success as a retrained model.

Boundary Unlearning

Abstract: The practical needs of the "right to be forgotten" and poisoned data removal call for efficient machine unlearning techniques, which enable machine learning models to unlearn, or to forget a fraction of training data and its lineage. Recent studies on machine unlearning for deep neural networks (DNNs) attempt to destroy the influence of the forgetting data by scrubbing the model parameters. However, it is prohibitively expensive due to the large dimension of the parameter space. In this paper, we refocus our attention from the parameter space to the decision space of the DNN model, and propose Boundary Unlearning, a rapid yet effective way to unlearn an entire class from a trained DNN model. The key idea is to shift the decision boundary of the original DNN model to imitate the decision behavior of the model retrained from scratch. We develop two novel boundary shift methods, namely Boundary Shrink and Boundary Expanding, both of which can rapidly achieve the utility and privacy guarantees. We extensively evaluate Boundary Unlearning on CIFAR-10 and Vggface2 datasets, and the results show that Boundary Unlearning can effectively forget the forgetting class on image classification and face recognition tasks, with an expected speed-up of 17× and 19×, respectively, compared with retraining from the scratch.

Membership Inference Attacks Against Machine Learning Models

Abstract: We quantitatively investigate how machine learning models leak information about the individual data records on which they were trained. We focus on the basic membership inference attack: given a data record and black-box access to a model, determine if the record was in the model's training dataset. To perform membership inference against a target model, we make adversarial use of machine learning and train our own inference model to recognize differences in the target model's predictions on the inputs that it trained on versus the inputs that it did not train on. We empirically evaluate our inference techniques on classification models trained by commercial "machine learning as a service" providers such as Google and Amazon. Using realistic datasets and classification tasks, including a hospital discharge dataset whose membership is sensitive from the privacy perspective, we show that these models can be vulnerable to membership inference attacks. We then investigate the factors that influence this leakage and evaluate mitigation strategies.

unlearned model
membership inference attack


To our surprise, some data augmentation strategies that showed better performance on WSOL (Cutout and CutMix #REFR ), showed the opposite results in our human-model alignment experiments.

Then, a plot representing EHR score at each threshold is obtained. Finally, the area under the curve (AUC) is computed. Let S c,λ

Experimental results on human-model alignment are given in Tab. 1.

Attribution maps calculated from baseline ResNet-50 scored the highest alignment with human-drawn bounding boxes.

This does not indicate that the mixed sample data augmentation techniques degrade localization ability.

Rather, it shows that the features used importantly for the prediction of models are less consistent with that of humans when the model is trained with the augmentation strategy.

CutMix and Salien-cyMix consistently score last or second last except for the IBA-EHR experiment, where the score difference between the four augmentation methods is marginal.

Analyzing Effects Of Mixed Sample Data Augmentation On Model Interpretability

Abstract: Data augmentation strategies are actively used when training deep neural networks (DNNs). Recent studies suggest that they are effective at various tasks. However, the effect of data augmentation on DNNs' interpretability is not yet widely investigated. In this paper, we explore the relationship between interpretability and data augmentation strategy in which models are trained with different data augmentation methods and are evaluated in terms of interpretability. To quantify the interpretability, we devise three evaluation methods based on alignment with humans, faithfulness to the model, and the number of humanrecognizable concepts in the model. Comprehensive experiments show that models trained with mixed sample data augmentation show lower interpretability, especially for CutMix and SaliencyMix augmentations. This new finding suggests that it is important to carefully adopt mixed sample data augmentation due to the impact on model interpretability, especially in mission-critical applications.

Cutmix: Regularization Strategy To Train Strong Classifiers With Localizable Features

Abstract: Regional dropout strategies have been proposed to enhance the performance of convolutional neural network classifiers. They have proved to be effective for guiding the model to attend on less discriminative parts of objects (e.g. leg as opposed to head of a person), thereby letting the network generalize better and have better object localization capabilities. On the other hand, current methods for regional dropout remove informative pixels on training images by overlaying a patch of either black pixels or random noise. Such removal is not desirable because it leads to information loss and inefficiency during training. We therefore propose the CutMix augmentation strategy: patches are cut and pasted among training images where the ground truth labels are also mixed proportionally to the area of the patches. By making efficient use of training pixels and retaining the regularization effect of regional dropout, CutMix consistently outperforms the state-of-the-art augmentation strategies on CIFAR and ImageNet classification tasks, as well as on the ImageNet weakly-supervised localization task. Moreover, unlike previous augmentation methods, our CutMix-trained ImageNet classifier, when used as a pretrained model, results in consistent performance gains in Pascal detection and MS-COCO image captioning benchmarks. We also show that CutMix improves the model robustness against input corruptions and its out-of-distribution detection performances. Source code and pretrained models are available at this https URL .


Understanding the quantum query complexity of Boolean functions f : D → {−1, 1}, where D is a subset of {−1, 1} n , has been a crucial task of quantum information science #REFR .

Query complexity is a model of computing where rigorous upper and lower bounds can be proven, which allows one to compare the power of quantum computers with classical ones.

Many celebrated quantum algorithms show an advantage in terms of query complexity, for example in unstructured search #OTHEREFR , period finding #OTHEREFR , Simon's problem #OTHEREFR , NAND-tree evaluation #OTHEREFR and element distinctness #OTHEREFR .

However, these advantages are limited to be polynomial in the case of total functions (those with D = {−1, 1} n ), while they can be exponential for highly structured problems (informally, this means that |D| = o(2 n )), such as for Simon's problem #OTHEREFR , period finding #OTHEREFR or k-fold forrelation #OTHEREFR .

It is widely believed that a lot of structure is necessary for superpolynomial speedups 1 .

The following folklore conjecture, which has circulated since the late 90s, but was first formally posed by Aaronson and Ambainis #OTHEREFR , formalizes this idea.

Influences Of Fourier Completely Bounded Polynomials And Classical Simulation Of Quantum Algorithms

Abstract: We give a new presentation of the main result of Arunachalam, Briët and Palazuelos (SICOMP'19) and show that quantum query algorithms are characterized by a new class of polynomials which we call Fourier completely bounded polynomials. We conjecture that all such polynomials have an influential variable. This conjecture is weaker than the famous Aaronson-Ambainis (AA) conjecture (Theory of Computing'14), but has the same implications for classical simulation of quantum query algorithms. We prove a new case of the AA conjecture by showing that it holds for homogeneous Fourier completely bounded polynomials. This implies that if the output of d-query quantum algorithm is a homogeneous polynomial p of degree 2d, then it has a variable with influence at least Var[p] 2. In addition, we give an alternative proof of the results of Bansal, Sinha and de Wolf (CCC'22 and QIP'23) showing that block-multilinear completely bounded polynomials have influential variables. Our proof is simpler, obtains better constants and does not use randomness. This research was supported by the European Union's Horizon 2020 research and innovation programme under the Marie Sk lodowska-Curie grant agreement no. 945045, and by the NWO Gravitation project NETWORKS under grant no. 024.002.003. 1 Recently, Yamakawa and Zhandry showed that superpolynomial speedups can be attained in unstructured search problems. However, that does not contradict the believe that structure is needed to achieve superpolynomial speedups in decision problems, which are those modeled by Boolean functions [YZ22].

Understanding Quantum Algorithms Via Query Complexity

Abstract: Query complexity is a model of computation in which we have to compute a function $f(x_1, \ldots, x_N)$ of variables $x_i$ which can be accessed via queries. The complexity of an algorithm is measured by the number of queries that it makes. Query complexity is widely used for studying quantum algorithms, for two reasons. First, it includes many of the known quantum algorithms (including Grover's quantum search and a key subroutine of Shor's factoring algorithm). Second, one can prove lower bounds on the query complexity, bounding the possible quantum advantage. In the last few years, there have been major advances on several longstanding problems in the query complexity. In this talk, we survey these results and related work, including: ::: - the biggest quantum-vs-classical gap for partial functions (a problem solvable with 1 query quantumly but requiring $\Omega(\sqrt{N})$ queries classically); ::: - the biggest quantum-vs-determistic and quantum-vs-probabilistic gaps for total functions (for example, a problem solvable with $M$ queries quantumly but requiring $\tilde{\Omega}(M^{2.5})$ queries probabilistically); ::: - the biggest probabilistic-vs-deterministic gap for total functions (a problem solvable with $M$ queries probabilistically but requiring $\tilde{\Omega}(M^{2})$ queries deterministically); ::: - the bounds on the gap that can be achieved for subclasses of functions (for example, symmetric functions); ::: - the connections between query algorithms and approximations by low-degree polynomials.