ali5341/scitldr-chat-format huggingface dataset:
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: The Cramer Distance as a Solution to Biased Wasserstein Gradients

Paper content:
The Wasserstein probability metric has received much attention from the machine learning community.
Unlike the Kullback-Leibler divergence, which strictly measures change in probability, the Wasserstein metric reflects the underlying geometry between outcomes.
The value of being sensitive to this geometry has been demonstrated, among others, in ordinal regression and generative modelling, and most recently in reinforcement learning.
In this paper we describe three natural properties of probability divergences that we believe reflect requirements from machine learning: sum invariance, scale sensitivity, and unbiased sample gradients.
The Wasserstein metric possesses the first two properties but, unlike the Kullback-Leibler divergence, does not possess the third.
We provide empirical evidence suggesting this is a serious issue in practice.
Leveraging insights from probabilistic forecasting we propose an alternative to the Wasserstein metric, the Cramér distance.
We show that the Cramér distance possesses all three desired properties, combining the best of the Wasserstein and Kullback-Leibler divergences.
We give empirical results on a number of domains comparing these three divergences.
To illustrate the practical relevance of the Cramér distance we design a new algorithm, the Cramér Generative Adversarial Network (GAN), and show that it has a number of desirable properties over the related Wasserstein GAN.

assistant: text: The manuscript proposes to use the Cramer distance to act as a loss when optimizing an objective function using stochastic gradient descent because it has unbiased sample gradients.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: PointGrow: Autoregressively Learned Point Cloud Generation with Self-Attention

Paper content:
A point cloud is an agile 3D representation, efficiently modeling an object's surface geometry.
However, these surface-centric properties also pose challenges on designing tools to recognize and synthesize point clouds.
This work presents a novel autoregressive model, PointGrow, which generates realistic point cloud samples from scratch or conditioned from given semantic contexts.
Our model operates recurrently, with each point sampled according to a conditional distribution given its previously-generated points.
Since point cloud object shapes are typically encoded by long-range interpoint dependencies, we augment our model with dedicated self-attention modules to capture these relations.
Extensive evaluation demonstrates that PointGrow achieves satisfying performance on both unconditional and conditional point cloud generation tasks, with respect to fidelity, diversity and semantic preservation.
Further, conditional PointGrow learns a smooth manifold of given images where 3D shape interpolation and arithmetic calculation can be performed inside.

assistant: text: An approach for generating 3D shapes as point clouds which considers the lexicographic ordering of points according to coordinates and trains a model to predict points in order.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Grouping-By-ID: Guarding Against Adversarial Domain Shifts

Paper content:
When training a deep neural network for supervised image classification, one can broadly distinguish between two types of latent features of images that will drive the classification of class Y. Following the notation of Gong et al. (2016), we can divide features broadly into the classes of
(i) “core” or “conditionally invariant” features X^ci whose distribution P(X^ci | Y) does not change substantially across domains and
(ii) “style” or “orthogonal” features X^orth whose distribution P(X^orth | Y) can change substantially across domains.
These latter orthogonal features would generally include features such as position, rotation, image quality or brightness but also more complex ones like hair color or posture for images of persons.
We try to guard against future adversarial domain shifts by ideally just using the “conditionally invariant” features for classification.
In contrast to previous work, we assume that the domain itself is not observed and hence a latent variable.
We can hence not directly see the distributional change of features across different domains.
We do assume, however, that we can sometimes observe a so-called identifier or ID variable.
We might know, for example, that two images show the same person, with ID referring to the identity of the person.
In data augmentation, we generate several images from the same original image, with ID referring to the relevant original image.
The method requires only a small fraction of images to have an ID variable.
We provide a causal framework for the problem by adding the ID variable to the model of Gong et al. (2016).
However, we are interested in settings where we cannot observe the domain directly and we treat domain as a latent variable.
If two or more samples share the same class and identifier, (Y, ID)=(y,i), then we treat those samples as counterfactuals under different style interventions on the orthogonal or style features.
Using this grouping-by-ID approach, we regularize the network to provide near constant output across samples that share the same ID by penalizing with an appropriate graph Laplacian.
This is shown to substantially improve performance in settings where domains change in terms of image quality, brightness, color changes, and more complex changes such as changes in movement and posture.
We show links to questions of interpretability, fairness and transfer learning.

assistant: text: The paper discusses ways to guard against adversarial domain shifts with counterfactual regularization by learning a classifier that is invariant to superficial changes (or "style" features) in imagess.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Uncertainty in Multitask Transfer Learning

Paper content:
Using variational Bayes neural networks, we develop an algorithm capable of accumulating knowledge into a prior from multiple different tasks.
This results in a rich prior capable of few-shot learning on new tasks.
The posterior can go beyond the mean field approximation and yields good uncertainty on the performed experiments.
Analysis on toy tasks show that it can learn from significantly different tasks while finding similarities among them.
Experiments on Mini-Imagenet reach state of the art with 74.5% accuracy on 5 shot learning.
Finally, we provide two new benchmarks, each showing a failure mode of existing meta learning algorithms such as MAML and prototypical Networks.

assistant: text: The work proposes a variational approach to meta-learning that employs latent variables corresponding to task-specific datasets.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning Function-Specific Word Representations

Paper content:
We present a neural framework for learning associations between interrelated groups of words such as the ones found in Subject-Verb-Object (SVO) structures.
Our model induces a joint function-specific word vector space, where vectors of e.g. plausible SVO compositions lie close together.
The model retains information about word group membership even in the joint space, and can thereby effectively be applied to a number of tasks reasoning over the SVO structure.
We show the robustness and versatility of the proposed framework by reporting state-of-the-art results on the tasks of estimating selectional preference (i.e., thematic fit) and event similarity.
The results indicate that the combinations of representations learned with our task-independent model outperform task-specific architectures from prior work, while reducing the number of parameters by up to 95%.
The proposed framework is versatile and holds promise to support learning function-specific representations beyond the SVO structures.

assistant: text: This paper proposes a neural network to learn function-specific work representations and demonstrates the advantage over alternatives.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: What do you learn from context? Probing for sentence structure in contextualized word representations

Paper content:
Contextualized representation models such as ELMo (Peters et al., 2018a) and BERT (Devlin et al., 2018) have recently achieved state-of-the-art results on a diverse array of downstream NLP tasks.
Building on recent token-level probing work, we introduce a novel edge probing task design and construct a broad suite of sub-sentence tasks derived from the traditional structured NLP pipeline.
We probe word-level contextual representations from four recent models and investigate how they encode sentence structure across a range of syntactic, semantic, local, and long-range phenomena.
We find that existing models trained on language modeling and translation produce strong representations for syntactic phenomena, but only offer comparably small improvements on semantic tasks over a non-contextual baseline.

assistant: text: Provides new insights on what is captured contextualized word embeddings by compiling a set of “edge probing” tasks.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Training GANs with Optimism

Paper content:
We address the issue of limit cycling behavior in training Generative Adversarial Networks and propose the use of Optimistic Mirror Decent (OMD) for training Wasserstein GANs.
Recent theoretical results have shown that optimistic mirror decent (OMD) can enjoy faster regret rates in the context of zero-sum games.
WGANs is exactly a context of solving a zero-sum game with simultaneous no-regret dynamics.
Moreover, we show that optimistic mirror decent addresses the limit cycling problem in training WGANs.
We formally show that in the case of bi-linear zero-sum games the last iterate of OMD dynamics converges to an equilibrium, in contrast to GD dynamics which are bound to cycle.
We also portray the huge qualitative difference between GD and OMD dynamics with toy examples, even when GD is modified with many adaptations proposed in the recent literature, such as gradient penalty or momentum.
We apply OMD WGAN training to a bioinformatics problem of generating DNA sequences.
We observe that models trained with OMD achieve consistently smaller KL divergence with respect to the true underlying distribution, than models trained with GD variants.
Finally, we introduce a new algorithm, Optimistic Adam, which is an optimistic variant of Adam.
We apply it to WGAN training on CIFAR10 and observe improved performance in terms of inception score as compared to Adam.

assistant: text: We propose the use of optimistic mirror decent to address cycling problems in the training of GANs. We also introduce the Optimistic Adam algorithm
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Structured Semi-Implicit Variational Inference

Paper content:
In this work we construct flexible joint distributions from low-dimensional conditional semi-implicit distributions.
Explicitly defining the structure of the approximation allows to make the variational lower bound tighter, resulting in more accurate inference.
Many recent advances in variational inference have been focused on different ways to estimate or bound the KL divergence between two complicated distributions.
They made it possible to perform variational inference with hierarchical distributions (Ranganath et al., 2016; Titsias and Ruiz, 2018; Sobolev and Vetrov, 2019) , semi-implicit distributions (Yin and Zhou, 2018; Molchanov et al., 2019) and even fully implicit distributions (Mescheder et al., 2017; Shi et al., 2017; Huszár, 2017) .
While these methods work well for low-dimensional cases, they can misbehave when the dimensionality of the problem grows.
In this work, we focus on semi-implicit variational inference, and consider structured multi-dimensional distributions.
We show that taking this structure into account, we can obtain a much tighter entropy bound and, consequentially, a much tighter evidence lower bound.
We also demonstrate that structured semi-implicit variational inference can successfully capture the multi-modal nature of the posterior distribution in deep Gaussian processes, and show a way to construct and learn an autoregressive semi-implicit model.

assistant: text: Utilizing the structure of distributions improves semi-implicit variational inference
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Frequency Principle: Fourier Analysis Sheds Light on Deep Neural Networks

Paper content:
We study the training process of Deep Neural Networks (DNNs) from the Fourier analysis perspective.
We demonstrate a very universal Frequency Principle (F-Principle) --- DNNs often fit target functions from low to high frequencies --- on high-dimensional benchmark datasets, such as MNIST/CIFAR10, and deep networks, such as VGG16.
This F-Principle of DNNs is opposite to the learning behavior of most conventional iterative numerical schemes (e.g., Jacobi method), which exhibits faster convergence for higher frequencies, for various scientific computing problems.
With a naive theory, we illustrate that this F-Principle results from the regularity of the commonly used activation functions.
The F-Principle implies an implicit bias that DNNs tend to fit training data by a low-frequency function.
This understanding provides an explanation of good generalization of DNNs on most real datasets and bad generalization of DNNs on parity function or randomized dataset.

assistant: text: This paper analyzes the loss of neural networks in the Fourier domain and finds that DNNs tend to learn low-frequency components before high-frequency ones.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: SELF: Learning to Filter Noisy Labels with Self-Ensembling

Paper content:
Deep neural networks (DNNs) have been shown to over-fit a dataset when being trained with noisy labels for a long enough time.
To overcome this problem, we present a simple and effective method self-ensemble label filtering (SELF) to progressively filter out the wrong labels during training.
Our method improves the task performance by gradually allowing supervision only from the potentially non-noisy (clean) labels and stops learning on the filtered noisy labels.
For the filtering, we form running averages of predictions over the entire training dataset using the network output at different training epochs.
We show that these ensemble estimates yield more accurate identification of inconsistent predictions throughout training than the single estimates of the network at the most recent training epoch.
While filtered samples are removed entirely from the supervised training loss, we dynamically leverage them via semi-supervised learning in the unsupervised loss.
We demonstrate the positive effect of such an approach on various image classification tasks under both symmetric and asymmetric label noise and at different noise ratios.
It substantially outperforms all previous works on noise-aware learning across different datasets and can be applied to a broad set of network architectures.

assistant: text: We propose a self-ensemble framework to train more robust deep learning models under noisy labeled datasets.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Robust Domain Randomization for Reinforcement Learning

Paper content:
Producing agents that can generalize to a wide range of environments is a significant challenge in reinforcement learning.
One method for overcoming this issue is domain randomization, whereby at the start of each training episode some parameters of the environment are randomized so that the agent is exposed to many possible variations.
However, domain randomization is highly inefficient and may lead to policies with high variance across domains.
In this work, we formalize the domain randomization problem, and show that minimizing the policy's Lipschitz constant with respect to the randomization parameters leads to low variance in the learned policies.
We propose a method where the agent only needs to be trained on one variation of the environment, and its learned state representations are regularized during training to minimize this constant.
We conduct experiments that demonstrate that our technique leads to more efficient and robust learning than standard domain randomization, while achieving equal generalization scores.

assistant: text: To improve the generalization ability of deep RL agents across the tasks with different visual patterns, this paper proposed a simple regularization technique for domain randomization.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Automatically Inferring Data Quality for Spatiotemporal Forecasting

Paper content:
Spatiotemporal forecasting has become an increasingly important prediction task in machine learning and statistics due to its vast applications, such as climate modeling, traffic prediction, video caching predictions, and so on.
While numerous studies have been conducted, most existing works assume that the data from different sources or across different locations are equally reliable.
Due to cost, accessibility, or other factors, it is inevitable that the data quality could vary, which introduces significant biases into the model and leads to unreliable prediction results.
The problem could be exacerbated in black-box prediction models, such as deep neural networks.
In this paper, we propose a novel solution that can automatically infer data quality levels of different sources through local variations of spatiotemporal signals without explicit labels.
Furthermore, we integrate the estimate of data quality level with graph convolutional networks to exploit their efficient structures.
We evaluate our proposed method on forecasting temperatures in Los Angeles.

assistant: text: We propose a method that infers the time-varying data quality level for spatiotemporal forecasting without explicitly assigned labels.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: A Flexible, Extensible Software Framework for Neural Net Compression

Paper content:
We propose a software framework based on ideas of the Learning-Compression algorithm , that allows one to compress any neural network by different compression mechanisms (pruning, quantization, low-rank, etc.).
By design, the learning of the neural net (handled by SGD) is decoupled from the compression of its parameters (handled by a signal compression function), so that the framework can be easily extended to handle different combinations of neural net and compression type.
In addition, it has other advantages, such as easy integration with deep learning frameworks, efficient training time, competitive practical performance in the loss-compression tradeoff, and reasonable convergence guarantees.
Our toolkit is written in Python and Pytorch and we plan to make it available by the workshop time, and eventually open it for contributions from the community.

assistant: text: We propose a software framework based on ideas of the Learning-Compression algorithm , that allows one to compress any neural network by different compression mechanisms (pruning, quantization, low-rank, etc.).
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: TequilaGAN: How To Easily Identify GAN Samples

Paper content:
In this paper we show strategies to easily identify fake samples generated with the Generative Adversarial Network framework.
One strategy is based on the statistical analysis and comparison of raw pixel values and features extracted from them.
The other strategy learns formal specifications from the real data and shows that fake samples violate the specifications of the real data.
We show that fake samples produced with GANs have a universal signature that can be used to identify fake samples.
We provide results on MNIST, CIFAR10, music and speech data.

assistant: text: Show that fake samples created with common generative adversarial network (GAN) implementations are easily identified using various statistical techniques.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: NEURAL EXECUTION ENGINES

Paper content:
Turing complete computation and reasoning are often regarded as necessary pre- cursors to general intelligence.
There has been a significant body of work studying neural networks that mimic general computation, but these networks fail to generalize to data distributions that are outside of their training set.
We study this problem through the lens of fundamental computer science problems: sorting and graph processing.
We modify the masking mechanism of a transformer in order to allow them to implement rudimentary functions with strong generalization.
We call this model the Neural Execution Engine, and show that it learns, through supervision, to numerically compute the basic subroutines comprising these algorithms with near perfect accuracy.
Moreover, it retains this level of accuracy while generalizing to unseen data and long sequences outside of the training distribution.

assistant: text: This paper deals with the problem of designing neural network architectures that can learn and implement general programs.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: V1Net: A computational model of cortical horizontal connections

Paper content:
The primate visual system builds robust, multi-purpose representations of the external world in order to support several diverse downstream cortical processes.
Such representations are required to be invariant to the sensory inconsistencies caused by dynamically varying lighting, local texture distortion, etc.
A key architectural feature combating such environmental irregularities is ‘long-range horizontal connections’ that aid the perception of the global form of objects.
In this work, we explore the introduction of such horizontal connections into standard deep convolutional networks; we present V1Net -- a novel convolutional-recurrent unit that models linear and nonlinear horizontal inhibitory and excitatory connections inspired by primate visual cortical connectivity.
We introduce the Texturized Challenge -- a new benchmark to evaluate object recognition performance under perceptual noise -- which we use to evaluate V1Net against an array of carefully selected control models with/without recurrent processing.
Additionally, we present results from an ablation study of V1Net demonstrating the utility of diverse neurally inspired horizontal connections for state-of-the-art AI systems on the task of object boundary detection from natural images.
We also present the emergence of several biologically plausible horizontal connectivity patterns, namely center-on surround-off, association fields and border-ownership connectivity patterns in a V1Net model trained to perform boundary detection on natural images from the Berkeley Segmentation Dataset 500 (BSDS500).
Our findings suggest an increased representational similarity between V1Net and biological visual systems, and highlight the importance of neurally inspired recurrent contextual processing principles for learning visual representations that are robust to perceptual noise and furthering the state-of-the-art in computer vision.

assistant: text: In this work, we present V1Net -- a novel recurrent neural network modeling cortical horizontal connections that give rise to robust visual representations through perceptual grouping.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Frequency-based Search-control in Dyna

Paper content:
Model-based reinforcement learning has been empirically demonstrated as a successful strategy to improve sample efficiency.
Particularly, Dyna architecture, as an elegant model-based architecture integrating learning and planning, provides huge flexibility of using a model.
One of the most important components in Dyna is called search-control, which refers to the process of generating state or state-action pairs from which we query the model to acquire simulated experiences.
Search-control is critical to improve learning efficiency.
In this work, we propose a simple and novel search-control strategy by searching high frequency region on value function.
Our main intuition is built on Shannon sampling theorem from signal processing, which indicates that a high frequency signal requires more samples to reconstruct.
We empirically show that a high frequency function is more difficult to approximate.
This suggests a search-control strategy: we should use states in high frequency region of the value function to query the model to acquire more samples.
We develop a simple strategy to locally measure the frequency of a function by gradient norm, and provide theoretical justification for this approach.
We then apply our strategy to search-control in Dyna, and conduct experiments to show its property and effectiveness on benchmark domains.

assistant: text: This paper proposes a new way to select states from which do do transitions in dyna algorithm.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Scoring-Aggregating-Planning: Learning task-agnostic priors from interactions and sparse rewards for zero-shot generalization

Paper content:
Humans can learn task-agnostic priors from interactive experience and utilize the priors for novel tasks without any finetuning.
In this paper, we propose Scoring-Aggregating-Planning (SAP), a framework that can learn task-agnostic semantics and dynamics priors from arbitrary quality interactions as well as the corresponding sparse rewards and then plan on unseen tasks in zero-shot condition.
The framework finds a neural score function for local regional state and action pairs that can be aggregated to approximate the quality of a full trajectory; moreover, a dynamics model that is learned with self-supervision can be incorporated for planning.
Many of previous works that leverage interactive data for policy learning either need massive on-policy environmental interactions or assume access to expert data while we can achieve a similar goal with pure off-policy imperfect data.
Instantiating our framework results in a generalizable policy to unseen tasks.
Experiments demonstrate that the proposed method can outperform baseline methods on a wide range of applications including gridworld, robotics tasks and video games.

assistant: text: We learn dense scores and dynamics model as priors from exploration data and use them to induce a good policy in new tasks in zero-shot condition.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Document Enhancement System Using Auto-encoders

Paper content:
The conversion of scanned documents to digital forms is performed using an Optical Character Recognition (OCR) software.
This work focuses on improving the quality of scanned documents in order to improve the OCR output.
We create an end-to-end document enhancement pipeline which takes in a set of noisy documents and produces clean ones.
Deep neural network based denoising auto-encoders are trained to improve the OCR quality.
We train a blind model that works on different noise levels of scanned text documents.
Results are shown for blurring and watermark noise removal from noisy scanned documents.
Scanned documents are stored as images and need to be processed by an Optical Character Recognition (OCR) software to extract the text contents into a digital format such as an ASCII text file.
This is an active research area and there are many tools in the market that process a scanned document and extract the content in a digital format.
The success with extraction of digital output is heavily dependent on the quality of the scanned document.
In practice, however, there is some noise associated with the scanned document.
Typical noises seen in scanned documents are blurring, watermarks, fading, and salt & pepper.
With the rise of deep learning adoption in computer vision tasks, there are many neural network models available for image denoising and restoration [1] .
However, most of the literature focuses on pictures (e.g., images from natural scenes) but not text documents, and the techniques used are not directly applicable due to very different nature of text document images.
The designed REDNET was successfully tested on deblurring document images with various levels of intensity as well as removing both gray-level and color watermarks from text image documents.
Currently, research on designing a unified network that can remove all noise types from text documents is ongoing.

assistant: text: We designed and tested a REDNET (ResNet Encoder-Decoder) with 8 skip connections to remove noise from documents, including blurring and watermarks, resulting in a high performance deep network for document image cleanup.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Dynamic Instance Hardness

Paper content:
We introduce dynamic instance hardness (DIH) to facilitate the training of machine learning models.
DIH is a property of each training sample and is computed as the running mean of the sample's instantaneous hardness as measured over the training history.
We use DIH to evaluate how well a model retains knowledge about each training sample over time.
We find that for deep neural nets (DNNs), the DIH of a sample in relatively early training stages reflects its DIH in later stages and as a result, DIH can be effectively used to reduce the set of training samples in future epochs.
Specifically, during each epoch, only samples with high DIH are trained (since they are historically hard) while samples with low DIH can be safely ignored.
DIH is updated each epoch only for the selected samples, so it does not require additional computation.
Hence, using DIH during training leads to an appreciable speedup.
Also, since the model is focused on the historically more challenging samples, resultant models are more accurate.
The above, when formulated as an algorithm, can be seen as a form of curriculum learning, so we call our framework DIH curriculum learning (or DIHCL).
The advantages of DIHCL, compared to other curriculum learning approaches, are: (1) DIHCL does not require additional inference steps over the data not selected by DIHCL in each epoch, (2) the dynamic instance hardness, compared to static instance hardness (e.g., instantaneous loss), is more stable as it integrates information over the entire training history up to the present time.
Making certain mathematical assumptions, we formulate the problem of DIHCL as finding a curriculum that maximizes a multi-set function $f(\cdot)$, and derive an approximation bound for a DIH-produced curriculum relative to the optimal curriculum.
Empirically, DIHCL-trained DNNs significantly outperform random mini-batch SGD and other recently developed curriculum learning methods in terms of efficiency, early-stage convergence, and final performance, and this is shown in training several state-of-the-art DNNs on 11 modern datasets.

assistant: text: This paper formulates DIH as a curriculum leaning problem that can more effectively utilize the data to train DNNs, and derives theory on the approximation bound.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: TFGAN: Improving Conditioning for Text-to-Video Synthesis

Paper content:
Developing conditional generative models for text-to-video synthesis is an extremely challenging yet an important topic of research in machine learning.
In this work, we address this problem by introducing Text-Filter conditioning Generative Adversarial Network (TFGAN), a GAN model with novel conditioning scheme that aids improving the text-video associations.
With a combination of this conditioning scheme and a deep GAN architecture, TFGAN generates photo-realistic videos from text on very challenging real-world video datasets.
In addition, we construct a benchmark synthetic dataset of moving shapes to systematically evaluate our conditioning scheme.
Extensive experiments demonstrate that TFGAN significantly outperforms the existing approaches, and can also generate videos of novel categories not seen during training.

assistant: text: An effective text-conditioning GAN framework for generating videos from text
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Point Cloud GAN

Paper content:
Generative Adversarial Networks (GAN) can achieve promising performance on learning complex data distributions on different types of data.
In this paper, we first show that a straightforward extension of an existing GAN algorithm is not applicable to point clouds, because the constraint required for discriminators is undefined for set data.
We propose a two fold modification to a GAN algorithm to be able to generate point clouds (PC-GAN).
First, we combine ideas from hierarchical Bayesian modeling and implicit generative models by learning a hierarchical and interpretable sampling process.
A key component of our method is that we train a posterior inference network for the hidden variables.
Second, PC-GAN defines a generic framework that can incorporate many existing GAN algorithms.
We further propose a sandwiching objective, which results in a tighter Wasserstein distance estimate than the commonly used dual form in WGAN.
We validate our claims on the ModelNet40 benchmark dataset and observe that PC- GAN trained by the sandwiching objective achieves better results on test data than existing methods.
We also conduct studies on several tasks, including generalization on unseen point clouds, latent space interpolation, classification, and image to point clouds transformation, to demonstrate the versatility of the proposed PC-GAN algorithm.

assistant: text: This paper proposes a new generative model for unordered data, with a particular application to point clouds, which includes an inference method and a novel objective function.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: 3D-SIC: 3D Semantic Instance Completion for RGB-D Scans

Paper content:
This paper introduces the task of semantic instance completion: from an incomplete RGB-D scan of a scene, we aim to detect the individual object instances comprising the scene and infer their complete object geometry.
This enables a semantically meaningful decomposition of a scanned scene into individual, complete 3D objects, including hidden and unobserved object parts.
This will open up new possibilities for interactions with object in a scene, for instance for virtual or robotic agents.
To address this task, we propose 3D-SIC, a new data-driven approach that jointly detects object instances and predicts their completed geometry.
The core idea of 3D-SIC is a novel end-to-end 3D neural network architecture that leverages joint color and geometry feature learning.
The fully-convolutional nature of our 3D network enables efficient inference of semantic instance completion for 3D scans at scale of large indoor environments in a single forward pass.
In a series evaluation, we evaluate on both real and synthetic scan benchmark data, where we outperform state-of-the-art approaches by over 15 in mAP@0.5 on ScanNet, and over 18 in mAP@0.5 on SUNCG.

assistant: text: The authors propose a novel end-to-end 3D convolutional network which predicts 3D semantic instance completion as object bounding boxes, class labels and complete object geometry.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Frequency Principle: Fourier Analysis Sheds Light on Deep Neural Networks

Paper content:
We study the training process of Deep Neural Networks (DNNs) from the Fourier analysis perspective.
We demonstrate a very universal Frequency Principle (F-Principle) --- DNNs often fit target functions from low to high frequencies --- on high-dimensional benchmark datasets, such as MNIST/CIFAR10, and deep networks, such as VGG16.
This F-Principle of DNNs is opposite to the learning behavior of most conventional iterative numerical schemes (e.g., Jacobi method), which exhibits faster convergence for higher frequencies, for various scientific computing problems.
With a naive theory, we illustrate that this F-Principle results from the regularity of the commonly used activation functions.
The F-Principle implies an implicit bias that DNNs tend to fit training data by a low-frequency function.
This understanding provides an explanation of good generalization of DNNs on most real datasets and bad generalization of DNNs on parity function or randomized dataset.

assistant: text: The paper studies the training process of NNs through Fourier analysis, concluding that NNs learn low frequency components before high frequency components.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Accelerating Convolutional Neural Networks using Iterative Two-Pass Decomposition

Paper content:
We present the iterative two-pass decomposition flow to accelerate existing convolutional neural networks (CNNs).
The proposed rank selection algorithm can effectively determine the proper ranks of the target convolutional layers for the low rank approximation.
Our two-pass CP-decomposition helps prevent from the instability problem.
The iterative flow makes the decomposition of the deeper networks systematic.
The experiment results shows that VGG16 can be accelerated with a 6.2x measured speedup while the accuracy drop remains only 1.2%.
Deep learning has become of vital importance in a variety of artificial intelligence applications.
Recently, convolutional neural networks (CNNs) have been widely applied to have the breakthrough in improving the recognition accuracy for challenging computer vision tasks such as image classification, localization, object detection, and so on BID19 ; BID11 ; BID20 ; BID3 ).
However, those significant achievements using CNNs come with the cost of larger network size and higher computational complexity, which leads to an increasing difficulty for deploying to resource constrained edge devices, or even for the fast computation on the cloud servers.
This paper addresses the acceleration of the existing CNN models to cope with such burden.
The iterative two-pass decomposition flow has been presented to accelerate existing deep CNNs.
Our two-pass decomposition effectively prevents from the CP instability.
The Rank Selection algorithm provides the fine-grained rank configuration to achieve the target speedup while maintaining the accuracy.
The experiment results show that VGG16 can be accelerated by 6.2 times with the accuracy drop of only 1.20% and the size reduction of 85%.
In addition, ResNet50 can be speeded up by 1.35 times with the accuracy drop of 1.51% and the size reduction of 48%.The
future works include the improvement of the grouping scheme and the decomposing order, and a smarter rank selection with non-linear fitness estimation. In
addition, accelerating 1×1 convolutional layers will also be considered for the further improvement on the advanced CNNs.APPENDIX A FIG6 shows the initial fitnesses of the baseline rank configuration and fitness-based configuration. Note
that the layer order is sorted by the fitnesses of the baseline rank configuration. Because
there is only a slight difference between the sorting of the two configuration, the fitness-based grouping scheme is based on the sorting of the baseline rank configuration. TAB1

assistant: text: This paper addresses the problem of learning a low rank tensor filter operation for filtering layers in deep neural networks (DNNs).
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Ridge Regression: Structure, Cross-Validation, and Sketching

Paper content:
We study the following three fundamental problems about ridge regression: (1) what is the structure of the estimator?
(2) how to correctly use cross-validation to choose the regularization parameter?
and (3) how to accelerate computation without losing too much accuracy?
We consider the three problems in a unified large-data linear model.
We give a precise representation of ridge regression as a covariance matrix-dependent linear combination of the true parameter and the noise.
We study the bias of $K$-fold cross-validation for choosing the regularization parameter, and propose a simple bias-correction.
We analyze the accuracy of primal and dual sketching for ridge regression, showing they are surprisingly accurate.
Our results are illustrated by simulations and by analyzing empirical data.

assistant: text: We study the structure of ridge regression in a high-dimensional asymptotic framework, and get insights about cross-validation and sketching.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks

Paper content:
Neural network pruning techniques can reduce the parameter counts of trained networks by over 90%, decreasing storage requirements and improving computational performance of inference without compromising accuracy.
However, contemporary experience is that the sparse architectures produced by pruning are difficult to train from the start, which would similarly improve training performance.
We find that a standard pruning technique naturally uncovers subnetworks whose initializations made them capable of training effectively.
Based on these results, we articulate the "lottery ticket hypothesis:" dense, randomly-initialized, feed-forward networks contain subnetworks ("winning tickets") that - when trained in isolation - reach test accuracy comparable to the original network in a similar number of iterations.
The winning tickets we find have won the initialization lottery: their connections have initial weights that make training particularly effective.
We present an algorithm to identify winning tickets and a series of experiments that support the lottery ticket hypothesis and the importance of these fortuitous initializations.
We consistently find winning tickets that are less than 10-20% of the size of several fully-connected and convolutional feed-forward architectures for MNIST and CIFAR10.
Above this size, the winning tickets that we find learn faster than the original network and reach higher test accuracy.

assistant: text: The paper examines the hypothesis that randomly initialized neural networks contain sub-networks that converge equally fast or faster and can reach the same or better classification accuracy
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Characterizing Missing Information in Deep Networks Using Backpropagated Gradients

Paper content:
Deep networks face challenges of ensuring their robustness against inputs that cannot be effectively represented by information learned from training data.
We attribute this vulnerability to the limitations inherent to activation-based representation.
To complement the learned information from activation-based representation, we propose utilizing a gradient-based representation that explicitly focuses on missing information.
In addition, we propose a directional constraint on the gradients as an objective during training to improve the characterization of missing information.
To validate the effectiveness of the proposed approach, we compare the anomaly detection performance of gradient-based and activation-based representations.
We show that the gradient-based representation outperforms the activation-based representation by 0.093 in CIFAR-10 and 0.361 in CURE-TSR datasets in terms of AUROC averaged over all classes.
Also, we propose an anomaly detection algorithm that uses the gradient-based representation, denoted as GradCon, and validate its performance on three benchmarking datasets.
The proposed method outperforms the majority of the state-of-the-art algorithms in CIFAR-10, MNIST, and fMNIST datasets with an average AUROC of 0.664, 0.973, and 0.934, respectively.

assistant: text: We propose a gradient-based representation for characterizing information that deep networks have not learned.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Improved Modeling of Complex Systems Using Hybrid Physics/Machine Learning/Stochastic Models

Paper content:
Combining domain knowledge models with neural models has been challenging.
End-to-end trained neural models often perform better (lower Mean Square Error) than domain knowledge models or domain/neural combinations, and the combination is inefficient to train.
In this paper, we demonstrate that by composing domain models with machine learning models, by using extrapolative testing sets, and invoking decorrelation objective functions, we create models which can predict more complex systems.
The models are interpretable, extrapolative, data-efficient, and capture predictable but complex non-stochastic behavior such as unmodeled degrees of freedom and systemic measurement noise.
We apply this improved modeling paradigm to several simulated systems and an actual physical system in the context of system identification.
Several ways of composing domain models with neural models are examined for time series, boosting, bagging, and auto-encoding on various systems of varying complexity and non-linearity.
Although this work is preliminary, we show that the ability to combine models is a very promising direction for neural modeling.

assistant: text: This paper conducts experiments to compare the extrapolative predictions of various hybrid models which compose physical models, neural networks and stochastic models, and tackles the challenge of unmodeled dynamics being a bottleneck.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Sample-efficient policy learning in multi-agent Reinforcement Learning via meta-learning

Paper content:
To gain high rewards in muti-agent scenes, it is sometimes necessary to understand other agents and make corresponding optimal decisions.
We can solve these tasks by first building models for other agents and then finding the optimal policy with these models.
To get an accurate model, many observations are needed and this can be sample-inefficient.
What's more, the learned model and policy can overfit to current agents and cannot generalize if the other agents are replaced by new agents.
In many practical situations, each agent we face can be considered as a sample from a population with a fixed but unknown distribution.
Thus we can treat the task against some specific agents as a task sampled from a task distribution.
We apply meta-learning method to build models and learn policies.
Therefore when new agents come, we can adapt to them efficiently.
Experiments on grid games show that our method can quickly get high rewards.

assistant: text: The paper presents an approach to multi-agent learning based on the framework of model-agnostic meta learning for the task of opponent modeling for multi-agent RL.