In the seminal work of #REFR , the authors studied an LQR problem with deterministic dynamics over an infinite horizon.

In order to maximize the accumulated reward over time, the agent learns to select actions based on past experiences (exploitation) and by making new choices (exploration).

In recent years, we have witnessed successful development of RL systems in various applications, including robotics control #OTHEREFR , AlphaGo and Atari games #OTHEREFR , autonomous driving #OTHEREFR , and stock trading #OTHEREFR .

Despite its practical success, theoretical understanding of RL is still limited and at its primitive stage.

To establish a better foundation of RL, there has been a surge of theoretical works in recent years on the Linear Quadratic Regulator (LQR) problem.

This problem is a special class of control problems with linear dynamics and quadratic cost functions #OTHEREFR .

They proved that the simple policy gradient method converges to the globally optimal solution with a linear rate (despite nonconvexity of the objective).

Their key idea is to utilize the Riccati equation (an algebraic-equation characterization that only works for LQR problems) and show that the cost function enjoys a "gradient dominance" property.

This result has been extended to other settings such as linear dynamics with additive or multiplicative Gaussian noise, finite-time horizon, and modifications of the vanilla policy-gradient method in follow-up works #OTHEREFR .

Other aspects in the learning of LQR, such as the trade-off between exploration and exploitation, have also been studied recently #OTHEREFR .

Despite the desirable theoretical properties of LQR, this setting is limited in practice due to the nonlinear nature of many real-world dynamic systems.

Policy Gradient Converges To The Globally Optimal Policy For Nearly Linear-Quadratic Regulators

Abstract: Nonlinear control systems with partial information to the decision maker are prevalent in a variety of applications. As a step toward studying such nonlinear systems, this work explores reinforcement learning methods for finding the optimal policy in the nearly linear-quadratic regulator systems. In particular, we consider a dynamic system that combines linear and nonlinear components, and is governed by a policy with the same structure. Assuming that the nonlinear component comprises kernels with small Lipschitz coefficients, we characterize the optimization landscape of the cost function. Although the cost function is nonconvex in general, we establish the local strong convexity and smoothness in the vicinity of the global optimizer. Additionally, we propose an initialization mechanism to leverage these properties. Building on the developments, we design a policy gradient algorithm that is guaranteed to converge to the globally optimal policy with a linear rate.

Global Convergence Of Policy Gradient Methods For The Linear Quadratic Regulator

Abstract: Direct policy gradient methods for reinforcement learning and continuous control problems are a popular approach for a variety of reasons: 1) they are easy to implement without explicit knowledge of the underlying model 2) they are an "end-to-end" approach, directly optimizing the performance metric of interest 3) they inherently allow for richly parameterized policies. A notable drawback is that even in the most basic continuous control problem (that of linear quadratic regulators), these methods must solve a non-convex optimization problem, where little is understood about their efficiency from both computational and statistical perspectives. In contrast, system identification and model based planning in optimal control theory have a much more solid theoretical footing, where much is known with regards to their computational and statistical properties. This work bridges this gap showing that (model free) policy gradient methods globally converge to the optimal solution and are efficient (polynomially so in relevant problem dependent quantities) with regards to their sample and computational complexities.


Comparison With Non-Adversarial Embedding Inversion

We have compared our results with the ones obtained using the non-adversarial embedding inversion approach by Dosovitskiy et al. in #REFR .

With access to the original data and to all layers of the original model (both missing in the adversarial settings), the approach in #OTHEREFR trains an inversion network minimizing the imagespace L2 loss.

In #OTHEREFR , the lowest reconstruction errors are obtained when the inversion network is trained together with the original model in the auto-encoder setup.

We adopt this case as our baseline, which provides an upper-bound to the reconstruction quality.

To enable the comparison with our approach, we implemented the baseline on ResNet50 face recognition model.

As shown in Figure 8 , despite the adversarial conditions, our attack can craft reconstructions that are close to the baseline ones.

On The Adversarial Inversion Of Deep Biometric Representations

Abstract: Biometric authentication service providers often claim that it is not possible to reverse-engineer a user's raw biometric sample, such as a fingerprint or a face image, from its mathematical (feature-space) representation. This is presented as a security feature of the system against an attacker who may be able to retrieve the template in the feature space. In this paper, we investigate this claim on the specific example of deep neural network (DNN) embeddings. Inversion of DNN embeddings has been investigated for explaining deep image representations or synthesizing normalized images. When setting the inversion, existing studies leverage full access to all layers of the original model, as well as all possible information on the original dataset-including, in some cases, the original image to be reconstructed. For the biometric authentication use case, we need to investigate this under adversarial settings where an attacker has access to a feature-space representation but no direct access to the exact original dataset nor the original learned model. Instead, we assume varying degree of attacker's background knowledge about the distribution of the dataset as well as the original learned model (architecture and training process). In the worst case, we assume attacker has no knowledge of the original data distribution nor the original model. In these cases, we show that the attacker can exploit off-the-shelf DNN models and public datasets, to mimic the behaviour of the original learned model to varying degrees of success, based only on the obtained representation and attacker's prior knowledge. We propose a two-pronged attack that first infers the original DNN by exploiting the model footprint on the embedding, and then reconstructs the raw data by using the inferred model. We show the practicality of the attack on popular DNNs trained for two prominent biometric modalities, face and fingerprint recognition. The attack can effectively infer the original recognition model (mean accuracy 83% for faces, 86% for fingerprints), and can craft effective biometric reconstructions that are successfully authenticated with 1-vs-1 authentication accuracy of up to 92% for some models.

Inverting Visual Representations With Convolutional Networks

Abstract: Feature representations, both hand-designed and learned ones, are often hard to analyze and interpret, even when they are extracted from visual data. We propose a new approach to study image representations by inverting them with an up-convolutional neural network. We apply the method to shallow representations (HOG, SIFT, LBP), as well as to deep networks. For shallow representations our approach provides significantly better reconstructions than existing methods, revealing that there is surprisingly rich information contained in these features. Inverting a deep network trained on ImageNet provides several insights into the properties of the feature representation learned by the network. Most strikingly, the colors and the rough contours of an image can be reconstructed from activations in higher network layers and even from the predicted class probabilities.


The noise audio clips in the categories of "natural", "music" and "babble" are sampled from the MUSAN dataset #REFR .

The implication of baseline and all is the same as Table 2 . CMLR afterward.

Our model is trained on 4 A100 GPUs with max tokens 2500, one batch size of data containing at most 100 seconds of audio per GPU.

It takes around 32 hours to finish a phase, where 400k steps are.

We do not use accumulated gradient, update freq is set to 1.

One thing to note is that, among all pre-training and fine-tuning stages, there is no extra noise added. Random noise is included in the decoding stage.

Practice Of The Conformer Enhanced Audio-Visual Hubert On Mandarin And English

Abstract: Considering the bimodal nature of human speech perception, lips, and teeth movement has a pivotal role in automatic speech recognition. Benefiting from the correlated and noise-invariant visual information, audiovisual recognition systems enhance robustness in multiple scenarios. In previous work, audiovisual HuBERT appears to be the finest practice incorporating modality knowledge. This paper outlines a mixed methodology, named conformer enhanced AV-HuBERT, boosting the AV-HuBERT system's performance a step further. Compared with baseline AV-HuBERT, our method in the one-phase evaluation of clean and noisy conditions achieves 7% and 16% relative WER reduction on the English AVSR benchmark dataset LRS3. Furthermore, we establish a novel 1000h Mandarin AVSR dataset CSTS. On top of the baseline AV-HuBERT, we exceed the WeNet ASR system by 14% and 18% relatively on MISP and CMLR by pre-training with this dataset. The conformerenhanced AV-HuBERT we proposed brings 7% on MISP and 6% CER reduction on CMLR, compared with the baseline AV-HuBERT system.

Musan: A Music, Speech, And Noise Corpus

Abstract: This report introduces a new corpus of music, speech, and noise. This dataset is suitable for training models for voice activity detection (VAD) and music/speech discrimination. Our corpus is released under a flexible Creative Commons license. The dataset consists of music from several genres, speech from twelve languages, and a wide assortment of technical and non-technical noises. We demonstrate use of this corpus for music/speech discrimination on Broadcast news and VAD for speaker identification.


Iv. Choosing A Spanning Tree Which Minimizes The Total Number Of Transmissions Used

For a single uniprior ICP represented by the informationflow graph G, since the bandwidth-optimal index code in #REFR gives a separate code for each strongly connected component of G, in the rest of this paper, we consider all informationflow graphs to be strongly connected.

Further, for an index code C T obtained from a spanning tree T , both the notations l max (C T ) as well as l max (T ) are used interchangeably to mean the maximum number of transmissions used to decode a single requested message at any receiver while using the index code C T . Example 1.

Consider the single uniprior ICP represented by the information-flow graph, G shown in Fig. 1 .

For this graph, Algorithm 2 in #OTHEREFR forms the connected graph on 4 labeled nodes and finds a spanning tree of diameter two.

There are four possible spanning trees of diameter two for a labeled K 4 , which are shown in Fig. 2 .

Corresponding to each of these spanning trees, the bandwidth-optimal index codes satisfying the min-max probability of error criterion are given in Table I .

Average Probability Of Error For Single Uniprior Index Coding Over Rayleigh Fading Channel

Abstract: Ong and Ho developed optimal linear index codes for single uniprior index coding problems (ICPs) by finding a spanning tree for each of the strongly connected components of the corresponding information-flow graphs, following which Thomas et al. considered the same class of ICPs over Rayleigh fading channel. They developed the min-max probability of error criterion for choosing an index code which minimized the probability of error at the receivers and showed that there always exist optimal linear index codes for which any receiver takes at most two transmissions to decode a requested message. Motivated by the above works, this paper considers single uniprior ICPs over Rayleigh fading channels for which minimizing average probability of error is shown to be a criterion for further selection of index codes. The optimal index code w.r.t this criterion is shown to be one that minimizes the total number of transmissions used for decoding the message requests at all the receivers. An algorithm that generates a spanning tree which has a lower value of this metric as compared to the optimal star graph is also presented. For a given set of parameters of single uniprior ICPs, a lower bound for the total number of transmissions used by any optimal index code is derived, and a class of ICPs for which this bound is tight is identified.

Optimal Index Codes For A Class Of Multicast Networks With Receiver Side Information

Abstract: This paper studies a special class of multicast index coding problems where a sender transmits messages to multiple receivers, each with some side information. Here, each receiver knows a unique message a priori, and there is no restriction on how many messages each receiver requests from the sender. For this class of multicast index coding problems, we obtain the optimal index code, which has the shortest codelength for which the sender needs to send in order for all receivers to obtain their (respective) requested messages. This is the first class of index coding problems where the optimal index codes are found. In addition, linear index codes are shown to be optimal for this class of index coding problems.


Table 8 shows how 3DPW changes the 3D pseudo-GTs compared to other ITW datasets, such as MPII, LSPET, and InstaVariety #REFR .

This shows that our recipes are specially designed for the annotation networks f to obtain beneficial 3D pseudo-GTs.

The reason for the small effect when the recipes are applied to the estimation networks g is that the estimation networks g are trained with 3D pseudo-GTs, while annotation networks f are trained with 2D GTs of ITW datasets without 3D evidence.

The absence of 3D evidence when training annotation networks f results in severe ambiguities, which can be cured by our recipes.

On the other hand, as estimation networks g are fully supervised with 3D pseudo-GTs, they suffer less from ambiguities.

For each recipe, None represents both annotation and estimation networks are trained with the remaining other two recipes. Effect of training annotation network f on 3DPW.

As the table shows, adding other ITW datasets does not obtain the performance gain of g compared to H36M+MI+COCO.

This is because adding ITW datasets does not contribute to relieving the depth ambiguity as they provide only 2D GTs.

On the other hand, 3DPW provides 3D GTs, largely helpful to alleviate the depth ambiguity.

Importantly, the 3D errors of g on MuPoTS decrease as well, which implies that using 3DPW as an additional training set is beneficial for multiple 3D ITW benchmarks.

It is noticeable that InstaVariety has 95 times more images than 3DPW, while much less helpful for the beneficial 3D pseudo-GTs.

Three Recipes For Better 3D Pseudo-Gts Of 3D Human Mesh Estimation In The Wild

Abstract: Recovering 3D human mesh in the wild is greatly challenging as in-the-wild (ITW) datasets provide only 2D pose ground truths (GTs). Recently, 3D pseudo-GTs have been widely used to train 3D human mesh estimation networks as the 3D pseudo-GTs enable 3D mesh supervision when training the networks on ITW datasets. However, despite the great potential of the 3D pseudo-GTs, there has been no extensive analysis that investigates which factors are important to make more beneficial 3D pseudo-GTs. In this paper, we provide three recipes to obtain highly beneficial 3D pseudo-GTs of ITW datasets. The main challenge is that only 2D-based weak supervision is allowed when obtaining the 3D pseudo-GTs. Each of our three recipes addresses the challenge in each aspect: depth ambiguity, sub-optimality of weak supervision, and implausible articulation. Experimental results show that simply retraining state-of-the-art networks with our new 3D pseudo-GTs elevates their performance to the next level without bells and whistles. The 3D pseudo-GT is publicly available 1 .

Learning 3D Human Dynamics From Video

Abstract: From an image of a person in action, we can easily guess the 3D motion of the person in the immediate past and future. This is because we have a mental model of 3D human dynamics that we have acquired from observing visual sequences of humans in motion. We present a framework that can similarly learn a representation of 3D dynamics of humans from video via a simple but effective temporal encoding of image features. At test time, from video, the learned temporal representation give rise to smooth 3D mesh predictions. From a single image, our model can recover the current 3D mesh as well as its 3D past and future motion. Our approach is designed so it can learn from videos with 2D pose annotations in a semi-supervised manner. Though annotated data is always limited, there are millions of videos uploaded daily on the Internet. In this work, we harvest this Internet-scale source of unlabeled data by training our model on unlabeled video with pseudo-ground truth 2D pose obtained from an off-the-shelf 2D pose detector. Our experiments show that adding more videos with pseudo-ground truth 2D pose monotonically improves 3D prediction performance. We evaluate our model on the recent challenging dataset of 3D Poses in the Wild and obtain state-of-the-art performance on the 3D prediction task without any fine-tuning. The project website with video can be found at https://akanazawa.github.io/human_dynamics/.


Researchers in #REFR provide a survey of interpretability and explainability related to ML algorithms, categorizing different interpretations proposed by different research works. Their categorization also pertains to medical applications.

In #OTHEREFR , the authors present a survey that explores the current trends and challenges of using visual analytics to interpret deep learning models based on XAI methods, as well as future directions of research in this area.

Two perspectives have been taken into consideration, model usage and visual approaches.

Model usage focuses on the performance of the AI system and how it behaves in different scenarios, while visual approaches examine the behavior of the AI system by looking at its outputs, such as predictions or classifications, over time.

The researchers identified several research questions in light of their findings, then discussed the research directions that could be pursued in the future.

By using XAI methods in the field of visual analytics, this survey provides guidance to better interpret neural networks.

According to the researchers, interpretability research can broadly be classified in many ways.

It can range from methods that provide clearly interpreted information to analysis of complex patterns.

The authors present attempts to mathematically formalize interpretability, as well as efforts to visualize it and evaluate its impact on task performance.

These different approaches aim to better understand and improve the interpretability of algorithms.

The investigators in #OTHEREFR provide a literature review of the use of XAI in the field of cybersecurity.

A Survey On Explainable Artificial Intelligence For Network Cybersecurity

Abstract: The "black-box" nature of artificial intelligence (AI) models has been the source of many concerns in their use for critical applications. Explainable Artificial Intelligence (XAI) is a rapidly growing research field that aims to create machine learning models that can provide clear and interpretable explanations for their decisions and actions. In the field of network cybersecurity, XAI has the potential to revolutionize the way we approach network security by enabling us to better understand the behavior of cyber threats and to design more effective defenses. In this survey, we review the state of the art in XAI for cybersecurity in network systems and explore the various approaches that have been proposed to address this important problem. The review follows a systematic classification of network-driven cybersecurity threats and issues. We discuss the challenges and limitations of current XAI methods in the context of cybersecurity and outline promising directions for future research.

A Survey On Explainable Artificial Intelligence (Xai): Towards Medical Xai

Abstract: Recently, artificial intelligence, especially machine learning has demonstrated remarkable performances in many tasks, from image processing to natural language processing, especially with the advent of deep learning. Along with research progress, machine learning has encroached into many different fields and disciplines. Some of them, such as the medical field, require high level of accountability, and thus transparency, which means we need to be able to explain machine decisions, predictions and justify their reliability. This requires greater interpretability, which often means we need to understand the mechanism underlying the algorithms. Unfortunately, the black-box nature of the deep learning is still unresolved, and many machine decisions are still poorly understood. We provide a review on interpretabilities suggested by different research works and categorize them, with the intention of providing alternative perspective that is hopefully more tractable for future adoption of interpretability standard. We explore further into interpretability in the medical field, illustrating the complexity of interpretability issue.


In Figure 5 , the poles generated by Xie et al. #REFR and Godard et al.

Although they perform well on visible regions, they are inefficient on handling the disoccluded regions.

Thanks to the two rectification strategies, our method is able to identify the error regions caused by the first stage and inpaint it with reasonable contents.

Specifically, the pruning-based rectification detects the intractable area of warping while the bidirectional matching rectification finds out the inconsistent pixels between the stereo views.

On the other hands, the exemplar synthesis results are shown in Figure 5 , and we can see that there are some artifacts in their results.

In particular, their synthesized stereo either produces blurring artifacts, or contains severe distortion.

#OTHEREFR (highlighted in the red rectangles) are distorted, while the ones generated by our method are very close to the ground-truth image.

Although the pole in the synthesized results produced by Luo et al. #OTHEREFR and Gonzalez et al.

#OTHEREFR are straight, their image details are not well preserved.

Also, the shape of traffic lights in Gonzalez et al. #OTHEREFR is blurry.

Moreover, the contour of the van in the green rectangles generated by previous methods is blurry or distorted, while our method can produce faithful result.

Single-View View Synthesis With Self-Rectified Pseudo-Stereo

Abstract: Synthesizing novel views from a single view image is a highly ill-posed problem. We discover an effective solution to reduce the learning ambiguity by expanding the single-view view synthesis problem to a multi-view setting. Specifically, we leverage the reliable and explicit stereo prior to generate a pseudostereo viewpoint, which serves as an auxiliary input to construct the 3D space. In this way, the challenging novel view synthesis process is decoupled into two simpler problems of stereo synthesis and 3D reconstruction. In order to synthesize a structurally correct and detail-preserved stereo image, we propose a selfrectified stereo synthesis to amend erroneous regions in an identify-rectify manner. Hard-to-train and incorrect warping samples are first discovered by two strategies, 1) pruning the network to reveal low-confident predictions; and 2) bidirectionally matching between stereo images to allow the discovery of improper mapping. These regions are then inpainted to form the final pseudo-stereo. With the aid of this extra input, a preferable 3D reconstruction can be easily obtained, and our

Deep3D: Fully Automatic 2D-To-3D Video Conversion With Deep Convolutional Neural Networks

Abstract: As 3D movie viewing becomes mainstream and the Virtual Reality (VR) market emerges, the demand for 3D contents is growing rapidly. Producing 3D videos, however, remains challenging. In this paper we propose to use deep neural networks to automatically convert 2D videos and images to a stereoscopic 3D format. In contrast to previous automatic 2D-to-3D conversion algorithms, which have separate stages and need ground truth depth map as supervision, our approach is trained end-to-end directly on stereo pairs extracted from existing 3D movies. This novel training scheme makes it possible to exploit orders of magnitude more data and significantly increases performance. Indeed, Deep3D outperforms baselines in both quantitative and human subject evaluations.


To overcome this issue, we develop a technique inspired by work in reinforcement learning #REFR .

These updates do not cause a significant overall decrease in optimizee's cost function.

This effect is more pronounced when we operate in parameter space as opposed to gradient space since the LSTM network does not get any information about the curvature of the optimization surface.

Running optimization in a finite horizon window in parameter space can then cause LSTM to suggest incorrect updates to the optimizee network.

In the specific case of QNNs, the parameters correspond to a rotation of given input state about a particular axis.

An incorrect update can very easily cause the quantum state to be rotated incorrectly and therefore lead to an increase in the value of the objective function we're interested in minimizing.

Throughout training, we keep track of past history of parameters and their corresponding cost function values in a "replay buffer".

At the start of training we instantiate a double ended queue dubbed as a replay-buffer B of a finite capacity R. For metaiteration t = 1 . . .

T we observe a history of parameters θ t and the corresponding cost C(θ t ).

If C(θ t+1 ) < C(θ t ) then we add the state s = [θ t+1 , C(θ t+1 ), ∆C(θ), h t+1 ] to the replay buffer.

Once the meta-iteration ends and L(Φ) is computed, if the QNN cost function is diverging, we seed the parameters for the next meta-iteration by performing the following update:

Learning To Optimize Quantum Neural Network Without Gradients

Abstract: Quantum Machine Learning is an emerging subfield in machine learning where one of the goals is to perform pattern recognition tasks by encoding data into quantum states. This extension from classical to quantum domain has been made possible due to the development of hybrid quantum-classical algorithms that allow a parameterized quantum circuit to be optimized using gradient based algorithms that run on a classical computer. The similarities in training of these hybrid algorithms and classical neural networks has further led to the development of Quantum Neural Networks (QNNs). However, in the current training regime for QNNs, the gradients w.r.t objective function have to be computed on the quantum device. This computation is highly non-scalable and is affected by hardware and sampling noise present in the current generation of quantum hardware. In this paper, we propose a training algorithm that does not rely on gradient information. Specifically, we introduce a novel metaoptimization algorithm that trains a meta-optimizer network to output parameters for the quantum circuit such that the objective function is minimized. We empirically and theoretically show that we achieve a better quality minima in fewer circuit evaluations than existing gradient based algorithms on different datasets.

Playing Atari With Deep Reinforcement Learning

Abstract: We present the first deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning. The model is a convolutional neural network, trained with a variant of Q-learning, whose input is raw pixels and whose output is a value function estimating future rewards. We apply our method to seven Atari 2600 games from the Arcade Learning Environment, with no adjustment of the architecture or learning algorithm. We find that it outperforms all previous approaches on six of the games and surpasses a human expert on three of them.


However, for the federated model, which has seen no training data (insuring the data privacy), these local updates are then collated, in our case via a block to be added to the blockchain, and averaged based on the FedAvg algorithm #REFR .

The goal for every model in the system, both the local models and the federated (global) model is to minimize the loss with respect to the model parameter: min

Where is a chosen loss function, consistent across all participating models, x, y are the training (input and desired output) vectors and ω is the model's parameters.

Hence, for each local model index m = 1, 2, ...., M of a participating IoT device, which has performed local training (1) either on the device itself or via a processing node, over its training set T m :

Iot Federated Blockchain Learning At The Edge

Abstract: IoT devices are sorely underutilised in the medical field, especially within machine learning for medicine, yet they offer unrivalled benefits. IoT devices are low cost, energy efficient, small and intelligent devices [1]. In this paper, we propose a distributed federated learning framework for IoT devices, more specifically for IoMT (Internet of Medical Things), using blockchain to allow for a decentralised scheme improving privacy and efficiency over a centralised system; this allows us to move from the cloud based architectures, that are prevalent, to the edge. The system is designed for three paradigms: 1) Training neural networks on IoT devices to allow for collaborative training of a shared model whilst decoupling the learning from the dataset [2] to ensure privacy [3]. Training is performed in an online manner simultaneously amongst all participants, allowing for training of actual data that may not have been present in a dataset collected in the traditional way and dynamically adapt the system whilst it is being trained. 2) Training of an IoMT system in a fully private manner such as to mitigate the issue with confidentiality of medical data and to build robust, and potentially bespoke [4], models where not much, if any, data exists. 3) Distribution of the actual network training, something federated learning itself does not do, to allow hospitals, for example, to utilize their spare computing resources to train network models.

Communication-Efficient Learning Of Deep Networks From Decentralized Data

Abstract: Modern mobile devices have access to a wealth of data suitable for learning models, which in turn can greatly improve the user experience on the device. For example, language models can improve speech recognition and text entry, and image models can automatically select good photos. However, this rich data is often privacy sensitive, large in quantity, or both, which may preclude logging to the data center and training there using conventional approaches. We advocate an alternative that leaves the training data distributed on the mobile devices, and learns a shared model by aggregating locally-computed updates. We term this decentralized approach Federated Learning. We present a practical method for the federated learning of deep networks based on iterative model averaging, and conduct an extensive empirical evaluation, considering five different model architectures and four datasets. These experiments demonstrate the approach is robust to the unbalanced and non-IID data distributions that are a defining characteristic of this setting. Communication costs are the principal constraint, and we show a reduction in required communication rounds by 10-100x as compared to synchronized stochastic gradient descent.


CLINC150 #REFR This dataset is multi-domain dataset which contains 23,700 utterances that cover 150 intent classes over 10 domains.

The dialogues cover a wide range of domains and topics.

MultiWOZ 2.2 #OTHEREFR is the improved version of MultiWOZ 2.1 #OTHEREFR ) that has corrected annotation errors, inconsistencies, and ontology issues, also added span annotations to standardize.

Banking 77 #OTHEREFR This dataset is a collection of 77 real-life customer banking service queries. It consists of 13,083 utterances.

Each query is labeled with a single intent, however, it is hard to differentiate because they correspond to very similar tasks.

Task-Optimized Adapters For An End-To-End Task-Oriented Dialogue System

Abstract: Task-Oriented Dialogue (TOD) systems are designed to carry out specific tasks by tracking dialogue states and generating appropriate responses to help users achieve defined goals. Recently, end-to-end dialogue models pre-trained based on large datasets have shown promising performance in the conversational system. However, they share the same parameters to train tasks of the dialogue system (NLU, DST, NLG), so debugging each task is challenging. Also, they require a lot of effort to fine-tune large parameters to create a taskoriented chatbot, making it difficult for nonexperts to handle. Therefore, we intend to train relatively lightweight and fast models compared to PLM. In this paper, we propose an End-to-end TOD system with Task-Optimized Adapters which learn independently per task, adding only small number of parameters after fixed layers of pre-trained network. We also enhance the performance of the DST and NLG modules through reinforcement learning, overcoming the learning curve that has lacked at the adapter learning and enabling the natural and consistent response generation that is appropriate for the goal. Our method is a model-agnostic approach and does not require prompt-tuning as only input data without a prompt. As results of the experiment, our method shows competitive performance on the MultiWOZ benchmark compared to the existing end-to-end models. In particular, we attain state-of-the-art performance on the DST task of 2.2 dataset.

An Evaluation Dataset For Intent Classification And Out-Of-Scope Prediction

Abstract: Task-oriented dialog systems need to know when a query falls outside their range of supported intents, but current text classification corpora only define label sets that cover every example. We introduce a new dataset that includes queries that are out-of-scope---i.e., queries that do not fall into any of the system's supported intents. This poses a new challenge because models cannot assume that every query at inference time belongs to a system-supported intent class. Our dataset also covers 150 intent classes over 10 domains, capturing the breadth that a production task-oriented agent must handle. We evaluate a range of benchmark classifiers on our dataset along with several different out-of-scope identification schemes. We find that while the classifiers perform well on in-scope intent classification, they struggle to identify out-of-scope queries. Our dataset and evaluation fill an important gap in the field, offering a way of more rigorously and realistically benchmarking text classification in task-driven dialog systems.


A weight decay is set to 0.01, with a linear warmup #REFR over t warm =1k iterations, followed by a linear decay.

Mapillary #OTHEREFR involves 18,000 training images and 2,000 validation images with diverse resolutions.

For brevity, we denote GTA, SYNTHIA, Cityscapes, BDD, and Mapillary as G, S, C, B, and M, respectively. Network architecture.

We conduct experiments using ResNet #OTHEREFR as an encoder architecture and DeepLabV3+ #OTHEREFR as a semantic segmentation decoder architecture.

In all experiments, encoders are initialized with an ImageNet #OTHEREFR pre-trained model. Training. We adopt an AdamW [38] optimizer.

An initial learning rate is set to 3×10 −5 for the encoder and 3 × 10 −4 for the decoder, 40k training iterations, a batch size of 4.

We use random scaling in the range [0.5, 2.0] and random cropping with a size of 768×768.

We apply additional data augmentation techniques, including random flipping and color jittering.

We set the texture regularization parameters as u l =5 × 10 −l−2 , and the texture generalization parameters as v l =5 × 10 −l−2 .

The original task loss and the stylized task loss weights are set to α orig =0.5 and α styl =0.5, respectively. We set the RSM threshold τ =0.1.

Texture Learning Domain Randomization For Domain Generalized Segmentation

Abstract: Deep Neural Networks (DNNs)-based semantic segmentation models trained on a source domain often struggle to generalize to unseen target domains, i.e., a domain gap problem. Texture often contributes to the domain gap, making DNNs vulnerable to domain shift because they are prone to be texture-biased. Existing Domain Generalized Semantic Segmentation (DGSS) methods have alleviated the domain gap problem by guiding models to prioritize shape over texture. On the other hand, shape and texture are two prominent and complementary cues in semantic segmentation. This paper argues that leveraging texture is crucial for improving performance in DGSS. Specifically, we propose a novel framework, coined Texture Learning Domain Randomization (TLDR). TLDR includes two novel losses to effectively enhance texture learning in DGSS: (1) a texture regularization loss to prevent overfitting to source domain textures by using texture features from an ImageNet pretrained model and (2) a texture generalization loss that utilizes random style images to learn diverse texture representations in a self-supervised manner. Extensive experimental results demonstrate the superiority of the proposed TLDR; e.g., TLDR achieves 46.5 mIoU on GTA→Cityscapes using ResNet-50, which improves the prior state-of-the-art method by 1.9 mIoU.

Accurate, Large Minibatch Sgd: Training Imagenet In 1 Hour

Abstract: Deep learning thrives with large neural networks and large datasets. However, larger networks and larger datasets result in longer training times that impede research and development progress. Distributed synchronous SGD offers a potential solution to this problem by dividing SGD minibatches over a pool of parallel workers. Yet to make this scheme efficient, the per-worker workload must be large, which implies nontrivial growth in the SGD minibatch size. In this paper, we empirically show that on the ImageNet dataset large minibatches cause optimization difficulties, but when these are addressed the trained networks exhibit good generalization. Specifically, we show no loss of accuracy when training with large minibatch sizes up to 8192 images. To achieve this result, we adopt a hyper-parameter-free linear scaling rule for adjusting learning rates as a function of minibatch size and develop a new warmup scheme that overcomes optimization challenges early in training. With these simple techniques, our Caffe2-based system trains ResNet-50 with a minibatch size of 8192 on 256 GPUs in one hour, while matching small minibatch accuracy. Using commodity hardware, our implementation achieves ~90% scaling efficiency when moving from 8 to 256 GPUs. Our findings enable training visual recognition models on internet-scale data with high efficiency.


Proposed by RegNet #REFR , it is the first Kitti split introduced for this task, and as shown by our results in Table II it remains the most challenging.

It provides enough samples and an accurate calibration ground truth.

Using the same dataset as previous works will help us compare our results.

However, those previous publications used different splits of the Kitti dataset.

We present those splits in Table I and will refer to them as α, β, γ, and δ.

From the splits presented in Table I , we choose α as our reference.

This is because it uses samples from separate days for training/validation and testing, with different camera intrinsics.

In comparison, splits β, γ, and δ used in other publications include in their testing sets samples recorded the same day and with the same camera intrinsics as their training sets.

δ even uses spatially redundant samples (some scenes are captured in the same location in training and testing sets) as acknowledged in #OTHEREFR .

The split and dataset choice are not trivial as results in Table II show that the same network (here UniCal) trained and tested on different splits will perform differently.

The splits with more similarities between the training and testing sets will get seemingly better results, whether those similarities are mostly in the camera intrinsics as in β, or even in the location where the samples were collected, as in δ.

Unical: A Single-Branch Transformer-Based Model For Camera-To-Lidar Calibration And Validation

Abstract: We introduce a novel architecture, UniCal, for Camera-to-LiDAR (C2L) extrinsic calibration which leverages self-attention mechanisms through a Transformer-based backbone network to infer the 6-degree of freedom (DoF) relative transformation between the sensors. Unlike previous methods, UniCal performs an early fusion of the input camera and LiDAR data by aggregating camera image channels and LiDAR mappings into a multi-channel unified representation before extracting their features jointly with a single-branch architecture. This single-branch architecture makes UniCal lightweight, which is desirable in applications with restrained resources such as autonomous driving. Through experiments, we show that UniCal achieves state-of-the-art results compared to existing methods. We also show that through transfer learning, weights learned on the calibration task can be applied to a calibration validation task without retraining the backbone.

Regnet: Multimodal Sensor Registration Using Deep Neural Networks

Abstract: In this paper, we present RegNet, the first deep convolutional neural network (CNN) to infer a 6 degrees of freedom (DOF) extrinsic calibration between multimodal sensors, exemplified using a scanning LiDAR and a monocular camera. Compared to existing approaches, RegNet casts all three conventional calibration steps (feature extraction, feature matching and global regression) into a single real-time capable CNN. Our method does not require any human interaction and bridges the gap between classical offline and target-less online calibration approaches as it provides both a stable initial estimation as well as a continuous online correction of the extrinsic parameters. During training we randomly decalibrate our system in order to train RegNet to infer the correspondence between projected depth measurements and RGB image and finally regress the extrinsic calibration. Additionally, with an iterative execution of multiple CNNs, that are trained on different magnitudes of decalibration, our approach compares favorably to state-of-the-art methods in terms of a mean calibration error of 0.28 degrees for the rotational and 6 cm for the translation components even for large decalibrations up to 1.5 m and 20 degrees.


Finally, we consider the LAD-LASSO loss function G LAD ℓ 1 w defined in #REFR .

In this case, we restrict ourselves to the real-valued case for the sake of simplicity.

In order to formulate the corresponding greedy selection rule, we need to introduce some auxiliary notation.

First, we define an augmented version A ∈ R (m+1)×N of the matrix A ∈ R m×N as A := A λw * or, equivalently, A ij :=

In addition, given x ∈ R N , we consider N augmentations of the residual vector r = Ax − y ∈ R N as the vectors r j ∈ R m+1 , defined by

The Greedy Side Of The Lasso: New Algorithms For Weighted Sparse Recovery Via Loss Function-Based Orthogonal Matching Pursuit

Abstract: We propose a class of greedy algorithms for weighted sparse recovery by considering new loss function-based generalizations of Orthogonal Matching Pursuit (OMP). Given a (regularized) loss function, the proposed algorithms alternate the iterative construction of the signal support via greedy index selection and a signal update based on solving a local data-fitting problem restricted to the current support. We show that greedy selection rules associated with popular weighted sparsity-promoting loss functions admit explicitly computable and simple formulas. Specifically, we consider ℓ 0-and ℓ 1-based versions of the weighted LASSO (Least Absolute Shrinkage and Selection Operator), the Square-Root LASSO (SR-LASSO) and the Least Absolute Deviations LASSO (LAD-LASSO). Through numerical experiments on Gaussian compressive sensing and high-dimensional function approximation, we demonstrate the effectiveness of the proposed algorithms and empirically show that they inherit desirable characteristics from the corresponding loss functions, such as SR-LASSO's noise-blind optimal parameter tuning and LAD-LASSO's fault tolerance. In doing so, our study sheds new light on the connection between greedy sparse recovery and convex relaxation.

Sparse Harmonic Transforms Ii: Best $S$-Term Approximation Guarantees For Bounded Orthonormal Product Bases In Sublinear-Time

Abstract: In this paper, we develop a sublinear-time compressive sensing algorithm for approximating functions of many variables which are compressible in a given Bounded Orthonormal Product Basis (BOPB). The resulting algorithm is shown to both have an associated best $s$-term recovery guarantee in the given BOPB, and also to work well numerically for solving sparse approximation problems involving functions contained in the span of fairly general sets of as many as $\sim10^{230}$ orthonormal basis functions. All code is made publicly available. ::: As part of the proof of the main recovery guarantee new variants of the well known CoSaMP algorithm are proposed which can utilize any sufficiently accurate support identification procedure satisfying a {Support Identification Property (SIP)} in order to obtain strong sparse approximation guarantees. These new CoSaMP variants are then proven to have both runtime and recovery error behavior which are largely determined by the associated runtime and error behavior of the chosen support identification method. The main theoretical results of the paper are then shown by developing a sublinear-time support identification algorithm for general BOPB sets which is robust to arbitrary additive errors. Using this new support identification method to create a new CoSaMP variant then results in a new robust sublinear-time compressive sensing algorithm for BOPB-compressible functions of many variables.


We have experimented with two large language models for code: (1) CodeT5 , which is an encoder-decoder model based on T5 #REFR and (2) Codex , which is a decoder only model based on GPT-3 (Brown et al., 2020) .

Both T5 and GPT-3 have strong zero-shot learning #OTHEREFR and transfer learning capabilities, and their versions for programming languages exhibit strong performance across multiple benchmarks.

The two models are of different sizesthe CodeT5 is using 700M parameters T5-large architecture, and the Codex model uses GPT-3 architecture with more than 100B parameters.

We have provided a more detailed discussion of these models in the Appendix, Section 7.3.

Next, we describe the methods we have employed to improve out-of-domain generalization for each model.

Exploring Distributional Shifts In Large Language Models For Code Analysis

Abstract: We systematically study the capacity of two large language models for code-CodeT5 and Codex-to generalize to out-of-domain data. In this study, we consider two fundamental applications-code summarization, and code generation. We split data into domains following its natural boundaries-by an organization, by a project, and by a module within the software project. This makes recognition of in-domain vs out-of-domain data at the time of deployment trivial. We establish that samples from each new domain present both models with a significant challenge of distribution shift. We study how well different established methods can adapt models to better generalize to new domains. Our experiments show that while multitask learning alone is a reasonable baseline, combining it with few-shot finetuning on examples retrieved from training data can achieve very strong performance. In fact, according to our experiments, this solution can outperform direct finetuning for very low-data scenarios. Finally, we consider variations of this approach to create a more broadly applicable method to adapt to multiple domains at once. We find that in the case of code generation, a model adapted to multiple domains simultaneously performs on par with those adapted to each domain individually.

Exploring The Limits Of Transfer Learning With A Unified Text-To-Text Transformer

Abstract: Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts every language problem into a text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration with scale and our new "Colossal Clean Crawled Corpus", we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. To facilitate future work on transfer learning for NLP, we release our dataset, pre-trained models, and code.


Most similar to our approach is arguably RAFT #REFR , which poses optical flow estimation as an optimization problem and uses a recurrent neural network to find an optimal descent direction.

Recent methods have investigated incorporating optimization problems into neural network architectures #OTHEREFR .

These methods design custom neural network layers that mimic certain canonical optimization problems.

As a result, parametrized optimization problems can be inserted into neural networks.

Not all optimization algorithms can easily be written as a layer and another strategy is to train a network to learn iterative updates from data (Adler &Öktem, 2018; #OTHEREFR .

In a similar vein, we aim to learn the steganography optimization problem with a convolutional neural network without specially designed optimization layers.

Learning Iterative Neural Optimizers For Im-Age Steganography

Abstract: Image steganography is the process of concealing secret information in images through imperceptible changes. Recent work has formulated this task as a classic constrained optimization problem. In this paper, we argue that image steganography is inherently performed on the (elusive) manifold of natural images, and propose an iterative neural network trained to perform the optimization steps. In contrast to classical optimization methods like L-BFGS or projected gradient descent, we train the neural network to also stay close to the manifold of natural images throughout the optimization. We show that our learned neural optimization is faster and more reliable than classical optimization approaches. In comparison to previous state-of-the-art encoder-decoder based steganography methods, it reduces the recovery error rate by multiple orders of magnitude and achieves zero error up to 3 bits per pixel (bpp) without the need for error-correcting codes.

Raft: Recurrent All-Pairs Field Transforms For Optical Flow

Abstract: We introduce Recurrent All-Pairs Field Transforms (RAFT), a new deep network architecture for optical flow. RAFT extracts per-pixel features, builds multi-scale 4D correlation volumes for all pairs of pixels, and iteratively updates a flow field through a recurrent unit that performs lookups on the correlation volumes. RAFT achieves state-of-the-art performance, with strong cross-dataset generalization and high efficiency in inference time, training speed, and parameter count. Code is available \url{https://github.com/princeton-vl/RAFT}.

recurrent neural network
optical flow estimation


Under certain conditions #REFR , this procedure is guaranteed to converge to a final global model representing an optimal consensus among the hospitals participating in the experiment.

The need for large amounts of data to develop Artificial Intelligence (AI) in healthcare has motivated a number of national and international initiatives aimed at creating medical data lakes accessible to researchers, such as the French Health Data Hub #OTHEREFR , the UK BioBank #OTHEREFR , the US ADNI #OTHEREFR and TCGA #OTHEREFR , among the many #OTHEREFR .

In spite of these initiatives, there are still major bottlenecks preventing the widespread availability of large centralized repositories of healthcare information #OTHEREFR .

To overcome these limitations, Federated Learning (FL) has been proposed as a working paradigm to enable the training of ML models on large datasets from diverse sources while guaranteeing the respect of data privacy and governance.

The basic paradigm of FL consists of iterating the following steps: i) model training is performed locally in the hospitals starting from a common initialization, ii) the resulting model parameters are subsequently shared (instead of the data) and aggregated, to define a global model iii) transmitted back to the hospitals to initiate a new local training step.

FL is particularly suited for applications in sensitive domains, such as healthcare and biomedical research #OTHEREFR .

The current societal and economical interest in FL for healthcare is paramount [56, #OTHEREFR , as demonstrated by the several largescale medical research projects based on FL at the national and international level, focusing for example on rare hematological diseases 1 , drug development 2 , blood cancer #OTHEREFR , among the many #OTHEREFR .

In spite of the current popularity, the real-world implementation of FL is complex and requires research and development actions at the crossroad between different domains spanning data science, software programming, networking, and security.

Today, several FL software frameworks are currently being proposed to data scientists and users, based on different design spaces, goals, and with varying degrees of software maturity.

Nevertheless, most of these frameworks are not designed to find seamless application in medical use-cases, due to the specific challenges and requirements of working with medical data and hospital infrastructures.

Fed-Biomed: Open, Transparent And Trusted Federated Learning For Real-World Healthcare Applications

Abstract: The real-world implementation of federated learning is complex and requires research and development actions at the crossroad between different domains ranging from data science, to software programming, networking, and security. While today several FL libraries are proposed to data scientists and users, most of these frameworks are not designed to find seamless application in medical use-cases, due to the specific challenges and requirements of working with medical data and hospital infrastructures. Moreover, governance, design principles, and security assumptions of these frameworks are generally not clearly illustrated, thus preventing the adoption in sensitive applications. Motivated by the current technological landscape of FL in healthcare, in this document we present


Since M 0 now includes continuous features we adopt Dueling DQN #REFR , a Deep RL algorithm.

In addition, we consider a ground MDP M 0,d at which the robot movements are modelled using continuous features.

The state space S 0 now contains continuous vectors (x, y, θ, v), representing pose and velocity of agent's mobile base on the plane.

The discrete set of actions A 0 allows to accelerate, decelerate, rotate, and a special action denotes the initiation of an interaction.

There exists goal MDPs, M 2 , M 1 , M 0 that capture both the dynamics and the task defined above, which can be obtained through a suitable composition of each M i,d and D #OTHEREFR .

Therefore, we can still apply our tecnique to the composed goal MDP.

The plot in Figure 6 shows a training comparison between the Dueling DQN agent alone (dot-dashed brown), and Dueling DQN receiving rewards from the grid abstraction (green).

As we can see, our method allows to provide useful exploration bias even in case of extremely sparse goal states, as in this case.

Exploiting Multiple Abstractions In Episodic Rl Via Reward Shaping

Abstract: One major limitation to the applicability of Reinforcement Learning (RL) to many practical domains is the large number of samples required to learn an optimal policy. To address this problem and improve learning efficiency, we consider a linear hierarchy of abstraction layers of the Markov Decision Process (MDP) underlying the target domain. Each layer is an MDP representing a coarser model of the one immediately below in the hierarchy. In this work, we propose a novel form of Reward Shaping where the solution obtained at the abstract level is used to offer rewards to the more concrete MDP, in such a way that the abstract solution guides the learning in the more complex domain. In contrast with other works in Hierarchical RL, our technique has few requirements in the design of the abstract models and it is also tolerant to modeling errors, thus making the proposed approach practical. We formally analyze the relationship between the abstract models and the exploration heuristic induced in the lower-level domain. Moreover, we prove that the method guarantees optimal convergence and we demonstrate its effectiveness experimentally.

Dueling Network Architectures For Deep Reinforcement Learning

Abstract: In recent years there have been many successes of using deep representations in reinforcement learning. Still, many of these applications use conventional architectures, such as convolutional networks, LSTMs, or auto-encoders. In this paper, we present a new neural network architecture for model-free reinforcement learning. Our dueling network represents two separate estimators: one for the state value function and one for the state-dependent action advantage function. The main benefit of this factoring is to generalize learning across actions without imposing any change to the underlying reinforcement learning algorithm. Our results show that this architecture leads to better policy evaluation in the presence of many similar-valued actions. Moreover, the dueling architecture enables our RL agent to outperform the state-of-the-art on the Atari 2600 domain.


In spite of the impressive performance of Deep Neural Networks (DNNs) in different fields, from computer vision to Natural Language Processing, these models are shown to be vulnerable to adversarial attacks, i.e., small perturbations of the input data #REFR .

In recent years many works have been proposed to evaluate the robustness of DNN models, and design methods to make them more robust to the perturbations to their inputs #OTHEREFR .

Neural Machine Translation (NMT) models, which take an input sentence and automatically generate its translation, have reached impressive performance by us-ing DNN models such as transformers #OTHEREFR .

Due to their performance, NMT models are widely used in different applications.

However, faulty outputs of such models may pose serious threats, especially in securityimportant applications.

Adversarial attacks against NMT models have been studied in the recent literature.

Targeted Adversarial Attacks Against Neural Machine Translation

Abstract: Neural Machine Translation (NMT) systems are used in various applications. However, it has been shown that they are vulnerable to very small perturbations of their inputs, known as adversarial attacks. In this paper, we propose a new targeted adversarial attack against NMT models. In particular, our goal is to insert a predefined target keyword into the translation of the adversarial sentence while maintaining similarity between the original sentence and the perturbed one in the source domain. To this aim, we propose an optimization problem, including an adversarial loss term and a similarity term. We use gradient projection in the embedding space to craft an adversarial sentence. Experimental results show that our attack outperforms Seq2Sick, the other targeted adversarial attack against NMT models, in terms of success rate and decrease in translation quality. Our attack succeeds in inserting a keyword into the translation for more than 75% of sentences while similarity with the original sentence stays preserved 1 .

Intriguing Properties Of Neural Networks

Abstract: Deep neural networks are highly expressive models that have recently achieved state of the art performance on speech and visual recognition tasks. While their expressiveness is the reason they succeed, it also causes them to learn uninterpretable solutions that could have counter-intuitive properties. In this paper we report two such properties. ::: First, we find that there is no distinction between individual high level units and random linear combinations of high level units, according to various methods of unit analysis. It suggests that it is the space, rather than the individual units, that contains of the semantic information in the high layers of neural networks. ::: Second, we find that deep neural networks learn input-output mappings that are fairly discontinuous to a significant extend. We can cause the network to misclassify an image by applying a certain imperceptible perturbation, which is found by maximizing the network's prediction error. In addition, the specific nature of these perturbations is not a random artifact of learning: the same perturbation can cause a different network, that was trained on a different subset of the dataset, to misclassify the same input.

adversarial attacks
Deep Neural Networks


We use a transformer #REFR encoder composed of 6 layers to model the high-order interactions on these features.

1) Polyline Encoder: All the three types of polylines are encoded by a shared polyline encoder based on attention mechanism. As illustrated in Fig.

3 , given a polyline After a MSA layer, the information of all other tokens is aggregated on the learnable token, which is treated as polyline feature h P .

2) Interaction-Aware Transformer: Social interaction modeling is a standard method to capture the interactions between agents #OTHEREFR , #OTHEREFR .

We go one step further by jointly modeling the interactions between agents, scene context, and occlusion for the inference task.

Each transformer layer consists of a MSA block followed by a position-wise fully connected feedforward network (FFN).

A residual connection is added after each block, followed by a LN.

Social Occlusion Inference With Vectorized Representation For Autonomous Driving

Abstract: Autonomous vehicles must be capable of handling the occlusion of the environment to ensure safe and efficient driving. In urban environment, occlusion often arises due to other vehicles obscuring the perception of the ego vehicle. Since the occlusion condition can impact the trajectories of vehicles, the behavior of other vehicles is helpful in making inferences about the occlusion as a remedy for perceptual deficiencies. This paper introduces a novel social occlusion inference approach that learns a mapping from agent trajectories and scene context to an occupancy grid map (OGM) representing the view of ego vehicle. Specially, vectorized features are encoded through the polyline encoder to aggregate features of vectors into features of polylines. A transformer module is then utilized to model the high-order interactions of polylines. Importantly, occlusion queries are proposed to fuse polyline features and generate the OGM without the input of visual modality. To verify the performance of vectorized representation, we design a baseline based on a fully transformer encoder-decoder architecture mapping the OGM with occlusion and historical trajectories information to the ground truth OGM. We evaluate our approach on an unsignalized intersection in the INTERACTION dataset, which outperforms the state-of-the-art results.

Attention Is All You Need

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


The vanilla MAP-Elites #REFR mutates randomly sampled solutions in the archive with a genetic operator; generated solutions are added to the archive if their objective value exceeds that of the solution currently occupying their corresponding archive cell.

The QD objective is to find, for each ∈ , a solution ∈ R such that ( ) = and ( ) is maximized:

However, since is continuous, this objective would require infinite memory to solve, so we relax the QD objective to finding an archive (i.e., a finite set) of representative solutions Θ ⊆ R .

A special case of QD is differentiable quality diversity (DQD) #OTHEREFR , where the objective and measure functions are first-order differentiable with gradients ∇ and ∇ .

Algorithms based on MAP-Elites #OTHEREFR tessellate the measure space into cells, and Θ is constrained such that each of its solutions falls into a different cell of the tessellation based on its measure values.

Since its inception, MAP-Elites extensions have included new genetic operators, such as the Iso+LineDD operator inspired by crossover #OTHEREFR , as well as new methods for tessellating the measure space to create the archive.

For example, MAP-Elites with Sliding Boundaries (MESB) adapts the size of grid cells online to reflect the distribution of solutions in measure space #OTHEREFR , while CVT-MAP-Elites #OTHEREFR precomputes a centroidal Voronoi tessellation (CVT) #OTHEREFR of the measure space that defines the archive cells.

Algorithms based on Novelty Search #OTHEREFR maintain an unstructured archive where each solution must be novel by being a certain distance away from its nearest neighbors in measure space.

A genetic algorithm then optimizes a population of solutions to achieve further novelty.

While Novelty Search itself is a purely diversity-driven approach, many of its successors are designed for QD; for instance, Novelty Search with Local Competition (NSLC) #OTHEREFR balances between optimizing for the objective and novelty via multi-objective evolutionary algorithms. QD algorithms have started to incorporate modern optimization algorithms.

Pyribs: A Bare-Bones Python Library For Quality Diversity Optimization

Abstract: Recent years have seen a rise in the popularity of quality diversity (QD) optimization, a branch of optimization that seeks to find a collection of diverse, high-performing solutions to a given problem. To grow further, we believe the QD community faces two challenges: developing a framework to represent the field's growing array of algorithms, and implementing that framework in software that supports a range of researchers and practitioners. To address these challenges, we have developed pyribs, a library built on a highly modular conceptual QD framework. By replacing components in the conceptual framework, and hence in pyribs, users can compose algorithms from across the QD literature; equally important, they can identify unexplored algorithm variations. Furthermore, pyribs makes this framework simple, flexible, and accessible, with a userfriendly API supported by extensive documentation and tutorials. This paper overviews the creation of pyribs, focusing on the conceptual framework that it implements and the design principles that have guided the library's development. Pyribs is available at https://pyribs.org CCS CONCEPTS • Computing methodologies → Search methodologies; • Software and its engineering → Software libraries and repositories.

Illuminating Search Spaces By Mapping Elites

Abstract: Many fields use search algorithms, which automatically explore a search space to find high-performing solutions: chemists search through the space of molecules to discover new drugs; engineers search for stronger, cheaper, safer designs, scientists search for models that best explain data, etc. The goal of search algorithms has traditionally been to return the single highest-performing solution in a search space. Here we describe a new, fundamentally different type of algorithm that is more useful because it provides a holistic view of how high-performing solutions are distributed throughout a search space. It creates a map of high-performing solutions at each point in a space defined by dimensions of variation that a user gets to choose. This Multi-dimensional Archive of Phenotypic Elites (MAP-Elites) algorithm illuminates search spaces, allowing researchers to understand how interesting attributes of solutions combine to affect performance, either positively or, equally of interest, negatively. For example, a drug company may wish to understand how performance changes as the size of molecules and their cost-to-produce vary. MAP-Elites produces a large diversity of high-performing, yet qualitatively different solutions, which can be more helpful than a single, high-performing solution. Interestingly, because MAP-Elites explores more of the search space, it also tends to find a better overall solution than state-of-the-art search algorithms. We demonstrate the benefits of this new algorithm in three different problem domains ranging from producing modular neural networks to designing simulated and real soft robots. Because MAP- Elites (1) illuminates the relationship between performance and dimensions of interest in solutions, (2) returns a set of high-performing, yet diverse solutions, and (3) improves finding a single, best solution, it will advance science and engineering.


Based on the findings with Non-IID data #REFR , we expect datasets with similar data distributions to provide similar Ψ(•) values.

The federated client clustering results, i.e., the subjective term in Equation 1, determines the performance of CFL to some extent.

Therefore, Equation 1motivates that the federated client clustering is a vital component in CFL.

We design a distribution extractor function Ψ(D) = Normalize( ∂ (ψ;D) ∂ψ ), which indicates the updated direction toward the local minimum corresponding for the input dataset D, anchor model ψ and loss function .

We do not optimize the anchor model ψ and maintain the loss function constant across all datasets in our implementations.

As a result, the Ψ(•) output can be viewed as a representation of data distribution corresponding to the input dataset.

Then, we use cosine similarity to evaluate the distribution similarity of the two decentralized datasets, i.e., given any two unknown datasets D i , D j , the similarity is determined as:

cos(Ψ(D i ), Ψ(D j )) = Ψ(D i ) • Ψ(D j ) Ψ(D i ) Ψ(D j ) .

To better support our assumptions, we implement observation experiments on cosine similarity, as shown in Figure 2 , in which we augment MNIST/Fashion-MNIST dataset and partition them with varying levels of augmentation.

The results reveal a significant difference in cosine similarity values.

As a result, we conclude that Ψ(•) could represent a local data distribution, and clients with similar local data distributions (at both label and feature levels) have higher cosine similarity.

Stochastic Clustered Federated Learning

Abstract: Federated learning is a distributed learning framework that takes full advantage of private data samples kept on edge devices. In real-world federated learning systems, these data samples are often decentralized and Non-Independently Identically Distributed (Non-IID), causing divergence and performance degradation in the federated learning process. As a new solution, clustered federated learning groups federated clients with similar data distributions to impair the Non-IID effects and train a better model for every cluster. This paper proposes StoCFL, a novel clustered federated learning approach for generic Non-IID issues. In detail, StoCFL implements a flexible CFL framework that supports an arbitrary proportion of client participation and newly joined clients for a varying FL system, while maintaining a great improvement in model performance. The intensive experiments are conducted by using four basic Non-IID settings and a real-world dataset. The results show that StoCFL could obtain promising cluster results even when the number of clusters is unknown. Based on the client clustering results, models trained with StoCFL outperform baseline approaches in a variety of contexts.

Federated Learning With Non-Iid Data

Abstract: Federated learning enables resource-constrained edge compute devices, such as mobile phones and IoT devices, to learn a shared model for prediction, while keeping the training data local. This decentralized approach to train models provides privacy, security, regulatory and economic benefits. In this work, we focus on the statistical challenge of federated learning when local data is non-IID. We first show that the accuracy of federated learning reduces significantly, by up to 55% for neural networks trained for highly skewed non-IID data, where each client device trains only on a single class of data. We further show that this accuracy reduction can be explained by the weight divergence, which can be quantified by the earth mover's distance (EMD) between the distribution over classes on each device and the population distribution. As a solution, we propose a strategy to improve training on non-IID data by creating a small subset of data which is globally shared between all the edge devices. Experiments show that accuracy can be increased by 30% for the CIFAR-10 dataset with only 5% globally shared data.


For example the errors of single view methods which estimate the position of people relative to the camera is on the order of 120mm where 100mm is in the depth direction #REFR . This is consistent with the presence of scale/depth ambiguity.

When estimating 3d position from camera data there is an inherent ambiguity due to scale/depth ambiguity.

Many types of interesting types of objects have a scale ambiguity, such as people, cars and animals.

The existence of scale ambiguity is reflected in the performance on competitive datasets.

In the multi view setting where the depth ambiguity can be eliminated the state of the art is on the order of 17mm #OTHEREFR .

There are many ways to resolve the scale/depth ambiguity.

One way to solve the problem is to add a depth sensor such as a lidar #OTHEREFR or structured light #OTHEREFR .

However adding additional sensors come with several drawbacks such as price, complexity, range among others.

For these reasons we will focus on methods which only use camera data.

Probabilistic 3D Regression With Projected Huber Distribution

Abstract: Estimating probability distributions which describe where an object is likely to be from camera data is a task with many applications. In this work we describe properties which we argue such methods should conform to. We also design a method which conform to these properties. In our experiments we show that our method produces uncertainties which correlate well with empirical errors. We also show that the mode of the predicted distribution outperform our regression baselines. The code for our implementation is available online Focal Length Model P(x,y,z;) P(x/z,y/z;) P(x, z;) Figure 1: Overview of out method. Our method takes an image as input and the focal length which was used to capture the scene and produces a log concave probability distribution in world coordinates in a way which can model the ambiguities which are inherent for camera sensors. On the left we have visualized the level curves in projected coordinates with the projected ground truth center of the cylinder. On the right we show the level curves of the distribution from a birds eye view. The estimated mode is shown as a cross and the ground truth position is a dot.

Camera Distance-Aware Top-Down Approach For 3D Multi-Person Pose Estimation From A Single Rgb Image

Abstract: Although significant improvement has been achieved recently in 3D human pose estimation, most of the previous methods only treat a single-person case. In this work, we firstly propose a fully learning-based, camera distance-aware top-down approach for 3D multi-person pose estimation from a single RGB image. The pipeline of the proposed system consists of human detection, absolute 3D human root localization, and root-relative 3D single-person pose estimation modules. Our system achieves comparable results with the state-of-the-art 3D single-person pose estimation models without any groundtruth information and significantly outperforms previous 3D multi-person pose estimation methods on publicly available datasets. ::: The code is available in this https URL , this https URL.


Existing Datasets And Methods For Visual Question Answering

The Microsoft COCO dataset #REFR is one of the large-scale datasets that impact many studies in computer vision tasks, including object detection, image classification, image captioning, and visual question answering.

In computer vision, the research purpose for VQA is to make computers understand the semantic context of images.

Several VQA datasets are built on the MS-COCO in different languages, such as the VQA #OTHEREFR , VQAv2 #OTHEREFR in English, FM-IQA #OTHEREFR for Chinese, the Japanese VQA #OTHEREFR for Japanese, and the ViVQA #OTHEREFR for Vietnamese.

There are also other two benchmark datasets for training and fine-tuning VQA methods, including Visual Genome (VG-QA) [15] and GQA #OTHEREFR . VG-QA is a VQA dataset that contains real-world photographs.

It is designed and constructed to emphasize the interactions and relationship between natural questions and particular regions on the images.

The creation of VG-QA lays the groundwork for building GQA, another large VQA collection that make use of Visual Genome scene graph structures to feature compositional question answering and real world reasoning.

Besides, in the natural language processing field, the SQuAD dataset #OTHEREFR has boosted many studies in question-answering and natural language understanding.

Integrating Image Features With Convolutional Sequence-To-Sequence Network For Multilingual Visual Question Answering

Abstract: Visual Question Answering (VQA) is a task that requires computers to give correct answers for the input questions based on the images. This task can be solved by humans with ease but is a challenge for computers. The VLSP2022-EVJVQA shared task carries the Visual Question Answering task in the multilingual domain on a newly released dataset: UIT-EVJVQA, in which the questions and answers are written in three different languages: English, Vietnamese and Japanese. We approached the challenge as a sequenceto-sequence learning task, in which we integrated hints from pre-trained state-of-the-art VQA models and image features with Convolutional Sequence-to-Sequence network to generate the desired answers. Our results obtained up to 0.3442 by F1 score on the public test set, 0.4210 on the private test set, and placed 3 rd in the competition.

Microsoft Coco: Common Objects In Context

Abstract: We present a new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding. This is achieved by gathering images of complex everyday scenes containing common objects in their natural context. Objects are labeled using per-instance segmentations to aid in precise object localization. Our dataset contains photos of 91 objects types that would be easily recognizable by a 4 year old. With a total of 2.5 million labeled instances in 328k images, the creation of our dataset drew upon extensive crowd worker involvement via novel user interfaces for category detection, instance spotting and instance segmentation. We present a detailed statistical analysis of the dataset in comparison to PASCAL, ImageNet, and SUN. Finally, we provide baseline performance analysis for bounding box and segmentation detection results using a Deformable Parts Model.


DW-Xcorr #REFR is a common similarity calculation method in the currently popular siamese network based trackers.

Specifically, in the process of similarity calculation, the generated similarity response map is difficult to focus on the target region, which will directly affect the effectiveness of feature decoding in the subsequent tracking head.

Secondly, the classification branch and the regression branch in the tracking head are separate in processing the task.

Specifically, the classification branch is responsible for distinguishing the target from the background, while the regression branch is responsible for locating the bounding box of all positive samples and does not consider the classification information.

It results in the accuracy misalignment between the output feature maps.

On the one hand, most of the methods #OTHEREFR [13] #OTHEREFR treat each channel in the feature map equally in the process of channel downscaling, making it difficult to focus the similarity response maps on the target region.

It convolves the two feature maps extracted by the siamese network channel by channel and outputs the final similarity response map.

The similarity response map has a feature that objects of the same category have a higher response on the same channel, while the response of other channels is suppressed.

However, existing siamese network based trackers usually use a modified ResNet-50 as the feature extraction network.

As a result, the number of channels of the final output feature map is too large, which leads to an elevated computational effort and makes it difficult to meet the real-time requirements of object tracking.

In order to reduce the computational effort, they #OTHEREFR [13] #OTHEREFR use 1×1 convolution to decrease the feature map's channels.

Siamthn: Siamese Target Highlight Network For Visual Tracking

Abstract: Siamese network based trackers develop rapidly in the field of visual object tracking in recent years. The majority of siamese network based trackers now in use treat each channel in the feature maps generated by the backbone network equally, making the similarity response map sensitive to background influence and hence challenging to focus on the target region. Additionally, there are no structural links between the classification and regression branches in these trackers, and the two branches are optimized separately during training. Therefore, there is a misalignment between the classification and regression branches, which results in less accurate tracking results. In this paper, a Target Highlight Module is proposed to help the generated similarity response maps to be more focused on the target region. To reduce the misalignment and produce more precise tracking results, we propose a corrective loss to train the model. The two branches of the model are jointly tuned with the use of corrective loss to produce more reliable prediction results. Experiments on 5 challenging benchmark datasets reveal that the method outperforms current models in terms of performance, and runs at 38 fps, proving its effectiveness and efficiency.

Siamrpn++: Evolution Of Siamese Visual Tracking With Very Deep Networks

Abstract: Siamese network based trackers formulate tracking as convolutional feature cross-correlation between target template and searching region. However, Siamese trackers still have accuracy gap compared with state-of-the-art algorithms and they cannot take advantage of feature from deep networks, such as ResNet-50 or deeper. In this work we prove the core reason comes from the lack of strict translation invariance. By comprehensive theoretical analysis and experimental validations, we break this restriction through a simple yet effective spatial aware sampling strategy and successfully train a ResNet-driven Siamese tracker with significant performance gain. Moreover, we propose a new model architecture to perform depth-wise and layer-wise aggregations, which not only further improves the accuracy but also reduces the model size. We conduct extensive ablation studies to demonstrate the effectiveness of the proposed tracker, which obtains currently the best results on four large tracking benchmarks, including OTB2015, VOT2018, UAV123, and LaSOT. Our model will be released to facilitate further studies based on this problem.

network based trackers
currently popular siamese


Our videos and text queries are collected from the HowTo100M #REFR dataset, and hence our category labels match theirs. As shown in Fig.

2 , the most frequently occurring categories (for all text-video pairs and just videos with step captions) are "Hobbies and Crafts", "Food and Entertaining", and "Home and Garden".

While these are the most common categories (similar to HowTo100M's most common categories), other categories still have a presence in our dataset.

We collected a total of 3.4K text-video pairs, which are 287 seconds long on average, with a total duration of 270 hours.

Out of 3.4K videos, 1.8K videos are clippable to a moment; i.e., only a short clip (<75% of the original video) is relevant to the text query.

Hierarchical Video-Moment Retrieval And Step-Captioning

Abstract: There is growing interest in searching for information from large video corpora. Prior works have studied relevant tasks, such as text-based video retrieval, moment retrieval, video summarization, and video captioning in isolation, without an end-to-end setup that can jointly search from video corpora and generate summaries. Such an endto-end setup would allow for many interesting applications, e.g., a text-based search that finds a relevant video from a video corpus, extracts the most relevant moment from that video, and segments the moment into important steps with captions. To address this, we present the HIREST (HIerarchical REtrieval and STep-captioning) dataset and propose a new benchmark that covers hierarchical information retrieval and visual/textual stepwise summarization from an instructional video corpus. HIREST consists of 3.4K text-video pairs from an instructional video dataset, where 1.1K videos have annotations of moment spans relevant to text query and breakdown of each moment into key instruction steps with caption and timestamps (totaling 8.6K step captions). Our hierarchical benchmark consists of video retrieval, moment retrieval, and two novel moment segmentation and step captioning tasks. In moment segmentation, models break down a video moment into instruction steps and identify start-end boundaries. In step captioning, models generate a textual summary for each step. We also present starting point task-specific and end-to-end joint baseline models for our new benchmark. While the baseline models show some promising results, there still exists large room for future improvement by the community.

Howto100M: Learning A Text-Video Embedding By Watching Hundred Million Narrated Video Clips

Abstract: Learning text-video embeddings usually requires a dataset of video clips with manually provided captions. However, such datasets are expensive and time consuming to create and therefore difficult to obtain on a large scale. In this work, we propose instead to learn such embeddings from video data with readily available natural language annotations in the form of automatically transcribed narrations. The contributions of this work are three-fold. First, we introduce HowTo100M: a large-scale dataset of 136 million video clips sourced from 1.22M narrated instructional web videos depicting humans performing and describing over 23k different visual tasks. Our data collection procedure is fast, scalable and does not require any additional manual annotation. Second, we demonstrate that a text-video embedding trained on this data leads to state-of-the-art results for text-to-video retrieval and action localization on instructional video datasets such as YouCook2 or CrossTask. Finally, we show that this embedding transfers well to other domains: fine-tuning on generic Youtube videos (MSR-VTT dataset) and movies (LSMDC dataset) outperforms models trained on these datasets alone. Our dataset, code and models will be publicly available at: www.di.ens.fr/willow/research/howto100m/.


For our implementation, we use GRUs instead of LSTMs because they are much faster while providing comparable performance #REFR .

We chose each baseline because it represents a family of similar approaches or is a well-cited approach used as a baseline in many papers.

attendgru This baseline is a simple unidirectional RNNbased attentional neural encoder-decoder architecture.

It takes only source code tokens as encoder input and English comment as decoder input. It was first introduced by Iyer et al.

#OTHEREFR as an off-the-shelf NMT/NLG approach to generate source code summaries.

transformer This baseline is another simple encoderdecoder architecture, but it replaces the recurrent layers with stacked muti-head attention layers #OTHEREFR .

As mentioned in Section II-B, transformers introduce a position embedding layer that captures the sequential order of tokens which allows the multi-head attention layer to process the entire sequence at the same time.

On the encoder side, the multi-head attention layer computes dot-product based self-attention on the source code tokens.

On the decoder side, there are two multi-head attention layers: a masked multi-head attention layer that computes self-attention on the comment tokens followed by a regular multi-head attention layer that computes attention between the encoder and the masked attention layer.

ast-attendgru This baseline is an enhancement over the attendgru model by including AST information on the encoder side along with source code tokens.

Label Smoothing Improves Neural Source Code Summarization

Abstract: Label smoothing is a regularization technique for neural networks. Normally neural models are trained to an output distribution that is a vector with a single 1 for the correct prediction, and 0 for all other elements. Label smoothing converts the correct prediction location to something slightly less than 1, then distributes the remainder to the other elements such that they are slightly greater than 0. A conceptual explanation behind label smoothing is that it helps prevent a neural model from becoming "overconfident" by forcing it to consider alternatives, even if only slightly. Label smoothing has been shown to help several areas of language generation, yet typically requires considerable tuning and testing to achieve the optimal results. This tuning and testing has not been reported for neural source code summarization-a growing research area in software engineering that seeks to generate natural language descriptions of source code behavior. In this paper, we demonstrate the effect of label smoothing on several baselines in neural code summarization, and conduct an experiment to find good parameters for label smoothing and make recommendations for its use.

A Neural Model For Generating Natural Language Summaries Of Program Subroutines

Abstract: Source code summarization -- creating natural language descriptions of source code behavior -- is a rapidly-growing research topic with applications to automatic documentation generation, program comprehension, and software maintenance. Traditional techniques relied on heuristics and templates built manually by human experts. Recently, data-driven approaches based on neural machine translation have largely overtaken template-based systems. But nearly all of these techniques rely almost entirely on programs having good internal documentation; without clear identifier names, the models fail to create good summaries. In this paper, we present a neural model that combines words from code with code structure from an AST. Unlike previous approaches, our model processes each data source as a separate input, which allows the model to learn code structure independent of the text in code. This process helps our approach provide coherent summaries in many cases even when zero internal documentation is provided. We evaluate our technique with a dataset we created from 2.1m Java methods. We find improvement over two baseline techniques from SE literature and one from NLP literature.


Our method to encode dialog data is inspired by the Sequential Sentence Classification (SCC) model #REFR which also is based on BERT encoder and organizes the dialog data in the hierarchical manner.

, and regard it as the representation of the ith utterance.

The representation of head token, T [CLS] , would be used in the following Gated Mechanism part as a kind of context information which encodes all the utterances.

With the utterance representations of T i[SEP ] , the role {r 1 , ..., r i } and intent {e i , ..., e i } information (one-hot vectors) are concatenated to their corresponding utterance representations.

After a MLP operation, we can obtain the final representation of each utterance by

T new i[SEP ] = M LP (r i ⊕ e i ⊕ T i[SEP ] ).

However, our model is different from SCC model in two aspects.

Firstly, the representation of head token [CLS] is additionally utilized as context information in the following modules.

Secondly, our encoder can integrate the extra role and intent information.

Gated Mechanism Enhanced Multi-Task Learning For Dialog Routing

Abstract: Currently, human-bot symbiosis dialog systems, e.g., pre-and after-sales in E-commerce, are ubiquitous, and the dialog routing component is essential to improve the overall efficiency, reduce human resource cost, and enhance user experience. Although most existing methods can fulfil this requirement, they can only model single-source dialog data and cannot effectively capture the underlying knowledge of relations among data and subtasks. In this paper, we investigate this important problem by thoroughly mining both the data-totask and task-to-task knowledge among various kinds of dialog data. To achieve the above targets, we propose a Gated Mechanism enhanced Multi-task Model (G3M), specifically including a novel dialog encoder and two tailored gated mechanism modules. The proposed method can play the role of hierarchical information filtering and is non-invasive to existing dialog systems. Based on two datasets collected from real world applications, extensive experimental results demonstrate the effectiveness of our method, which achieves the state-of-the-art performance by improving 8.7%/11.8% on RMSE metric and 2.2%/4.4% on F1 metric.

Pretrained Language Models For Sequential Sentence Classification

Abstract: As a step toward better document-level understanding, we explore classification of a sequence of sentences into their corresponding categories, a task that requires understanding sentences in context of the document. Recent successful models for this task have used hierarchical models to contextualize sentence representations, and Conditional Random Fields (CRFs) to incorporate dependencies between subsequent labels. In this work, we show that pretrained language models, BERT (Devlin et al., 2018) in particular, can be used for this task to capture contextual dependencies without the need for hierarchical encoding nor a CRF. Specifically, we construct a joint sentence representation that allows BERT Transformer layers to directly utilize contextual information from all words in all sentences. Our approach achieves state-of-the-art results on four datasets, including a new dataset of structured scientific abstracts.

dialog data
Sequential Sentence Classification