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

Title: Bounding and Counting Linear Regions of Deep Neural Networks

Paper content:
In this paper, we study the representational power of deep neural networks (DNN) that belong to the family of piecewise-linear (PWL) functions, based on PWL activation units such as rectifier or maxout.
We investigate the complexity of such networks by studying the number of linear regions of the PWL function.
Typically, a PWL function from a DNN can be seen as a large family of linear functions acting on millions of such regions.
We directly build upon the work of Mont´ufar et al. (2014), Mont´ufar (2017), and Raghu et al. (2017) by refining the upper and lower bounds on the number of linear regions for rectified and maxout networks.
In addition to achieving tighter bounds, we also develop a novel method to perform exact numeration or counting of the number of linear regions with a mixed-integer linear formulation that maps the input space to output.
We use this new capability to visualize how the number of linear regions change while training DNNs.

assistant: text: This paper presents improved bounds for counting the number of linear regions in ReLU networks.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: NeuroFabric: Identifying Ideal Topologies for Training A Priori Sparse Networks

Paper content:
Long training times of deep neural networks are a bottleneck in machine learning research.
The major impediment to fast training is the quadratic growth of both memory and compute requirements of dense and convolutional layers with respect to their information bandwidth.
Recently, training `a priori' sparse networks has been proposed as a method for allowing layers to retain high information bandwidth, while keeping memory and compute low.
However, the choice of which sparse topology should be used in these networks is unclear.
In this work, we provide a theoretical foundation for the choice of intra-layer topology.
First, we derive a new sparse neural network initialization scheme that allows us to explore the space of very deep sparse networks.
Next, we evaluate several topologies and show that seemingly similar topologies can often have a large difference in attainable accuracy.
To explain these differences, we develop a data-free heuristic that can evaluate a topology independently from the dataset the network will be trained on.
We then derive a set of requirements that make a good topology, and arrive at a single topology that satisfies all of them.

assistant: text: The authors propose to replace dense layers with sparsely-connected linear layers and an approach to finding the best topology by measuring how well the sparse layers approximate random weights of their dense counterparts.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Experience replay for continual learning

Paper content:
Continual learning is the problem of learning new tasks or knowledge while protecting old knowledge and ideally generalizing from old experience to learn new tasks faster.
Neural networks trained by stochastic gradient descent often degrade on old tasks when trained successively on new tasks with different data distributions.
This phenomenon, referred to as catastrophic forgetting, is considered a major hurdle to learning with non-stationary data or sequences of new tasks, and prevents networks from continually accumulating knowledge and skills.
We examine this issue in the context of reinforcement learning, in a setting where an agent is exposed to tasks in a sequence.
Unlike most other work, we do not provide an explicit indication to the model of task boundaries, which is the most general circumstance for a learning agent exposed to continuous experience.
While various methods to counteract catastrophic forgetting have recently been proposed, we explore a straightforward, general, and seemingly overlooked solution - that of using experience replay buffers for all past events - with a mixture of on- and off-policy learning, leveraging behavioral cloning.
We show that this strategy can still learn new tasks quickly yet can substantially reduce catastrophic forgetting in both Atari and DMLab domains, even matching the performance of methods that require task identities.
When buffer storage is constrained, we confirm that a simple mechanism for randomly discarding data allows a limited size buffer to perform almost as well as an unbounded one.

assistant: text: We show that, in continual learning settings, catastrophic forgetting can be avoided by applying off-policy RL to a mixture of new and replay experience, with a behavioral cloning loss.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Block-Sparse Recurrent Neural Networks

Paper content:
Recurrent Neural Networks (RNNs) are used in state-of-the-art models in domains such as speech recognition, machine translation, and language modelling.
Sparsity is a technique to reduce compute and memory requirements of deep learning models.
Sparse RNNs are easier to deploy on devices and high-end server processors.
Even though sparse operations need less compute and memory relative to their dense counterparts, the speed-up observed by using sparse operations is less than expected on different hardware platforms.
In order to address this issue, we investigate two different approaches to induce block sparsity in RNNs: pruning blocks of weights in a layer and using group lasso regularization with pruning to create blocks of weights with zeros.
Using these techniques, we can create block-sparse RNNs with sparsity ranging from 80% to 90% with a small loss in accuracy.
This technique allows us to reduce the model size by roughly 10x.
Additionally, we can prune a larger dense network to recover this loss in accuracy while maintaining high block sparsity and reducing the overall parameter count.
Our technique works with a variety of block sizes up to 32x32.
Block-sparse RNNs eliminate overheads related to data storage and irregular memory accesses while increasing hardware efficiency compared to unstructured sparsity.

assistant: text: We show the RNNs can be pruned to induce block sparsity which improves speedup for sparse operations on existing hardware
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Near-Optimal Representation Learning for Hierarchical Reinforcement Learning

Paper content:
We study the problem of representation learning in goal-conditioned hierarchical reinforcement learning.
In such hierarchical structures, a higher-level controller solves tasks by iteratively communicating goals which a lower-level policy is trained to reach.
Accordingly, the choice of representation -- the mapping of observation space to goal space -- is crucial.
To study this problem, we develop a notion of sub-optimality of a representation, defined in terms of expected reward of the optimal hierarchical policy using this representation.
We derive expressions which bound the sub-optimality and show how these expressions can be translated to representation learning objectives which may be optimized in practice.
Results on a number of difficult continuous-control tasks show that our approach to representation learning yields qualitatively better representations as well as quantitatively better hierarchical policies, compared to existing methods.

assistant: text: We translate a bound on sub-optimality of representations to a practical training objective in the context of hierarchical reinforcement learning.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Discriminative out-of-distribution detection for semantic segmentation

Paper content:
Most classification and segmentation datasets assume a closed-world scenario in which predictions are expressed as distribution over a predetermined set of visual classes.
However, such assumption implies unavoidable and often unnoticeable failures in presence of out-of-distribution (OOD) input.
These failures are bound to happen in most real-life applications since current visual ontologies are far from being comprehensive.
We propose to address this issue by discriminative detection
of OOD pixels in input data.
Different from recent approaches, we avoid to bring any decisions by only observing the training dataset of the primary model trained to solve the desired computer vision task.
Instead, we train a dedicated OOD model
which discriminates the primary training set from a much larger "background" dataset which approximates the variety of the visual world.
We perform our experiments on high resolution natural images in a dense prediction setup.
We use several road driving datasets as our training distribution, while we approximate the background distribution with the ILSVRC dataset.
We evaluate our approach on WildDash test, which is currently the only public test dataset with out-of-distribution images.
The obtained results show that the proposed approach succeeds to identify out-of-distribution pixels while outperforming previous work by a wide margin.

assistant: text: We present a novel approach for detecting out-of-distribution pixels in semantic segmentation.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: DISENTANGLED STATE SPACE MODELS: UNSUPERVISED LEARNING OF DYNAMICS ACROSS HETEROGENEOUS ENVIRONMENTS

Paper content:
Sequential data often originates from diverse environments.
Across them exist both shared regularities and environment specifics.
To learn robust cross-environment descriptions of sequences we introduce disentangled state space models (DSSM).
In the latent space of DSSM environment-invariant state dynamics is explicitly disentangled from environment-specific information governing that dynamics.
We empirically show that such separation enables robust prediction, sequence manipulation and environment characterization.
We also propose an unsupervised VAE-based training procedure to learn DSSM as Bayesian filters.
In our experiments, we demonstrate state-of-the-art performance in controlled generation and prediction of bouncing ball video sequences across varying gravitational influences.

assistant: text: The paper presents a generative state space model using a global latent variable E to capture environment-specific information.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Adversarial Neural Pruning

Paper content:
Despite the remarkable performance of deep neural networks (DNNs) on various tasks, they are susceptible to adversarial perturbations which makes it difficult to deploy them in real-world safety-critical applications.
In this paper, we aim to obtain robust networks by sparsifying DNN's latent features sensitive to adversarial perturbation.
Specifically, we define vulnerability at the latent feature space and then propose a Bayesian framework to prioritize/prune features based on their contribution to both the original and adversarial loss.
We also suggest regularizing the features' vulnerability during training to improve robustness further.
While such network sparsification has been primarily studied in the literature for computational efficiency and regularization effect of DNNs, we confirm that it is also useful to design a defense mechanism through quantitative evaluation and qualitative analysis.
We validate our method, \emph{Adversarial Neural Pruning (ANP)} on multiple benchmark datasets, which results in an improvement in test accuracy and leads to state-of-the-art robustness.
ANP also tackles the practical problem of obtaining sparse and robust networks at the same time, which could be crucial to ensure adversarial robustness on lightweight networks deployed to computation and memory-limited devices.

assistant: text: We propose a novel method for suppressing the vulnerability of latent feature space to achieve robust and compact networks.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Cross-Dimensional Self-Attention for Multivariate, Geo-tagged Time Series Imputation

Paper content:
Many real-world applications involve multivariate, geo-tagged time series data: at each location, multiple sensors record corresponding measurements.
For example, air quality monitoring system records PM2.5, CO, etc.
The resulting time-series data often has missing values due to device outages or communication errors.
In order to impute the missing values, state-of-the-art methods are built on Recurrent Neural Networks (RNN), which process each time stamp sequentially, prohibiting the direct modeling of the relationship between distant time stamps.
Recently, the self-attention mechanism has been proposed for sequence modeling tasks such as machine translation, significantly outperforming RNN because the relationship between each two time stamps can be modeled explicitly.
In this paper, we are the first to adapt the self-attention mechanism for multivariate, geo-tagged time series data.
In order to jointly capture the self-attention across different dimensions (i.e. time, location and sensor measurements) while keep the size of attention maps reasonable, we propose a novel approach called Cross-Dimensional Self-Attention (CDSA) to process each dimension sequentially, yet in an order-independent manner.
On three real-world datasets, including one our newly collected NYC-traffic dataset, extensive experiments demonstrate the superiority of our approach compared to state-of-the-art methods for both imputation and forecasting tasks.

assistant: text: This paper empirically studies the effectiveness of transformer models for time series data imputation across dimensions of the input.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Deep Rewiring: Training very sparse deep networks

Paper content:
Neuromorphic hardware tends to pose limits on the connectivity of deep networks that one can run on them.
But also generic hardware and software implementations of deep learning run more efficiently for sparse networks.
Several methods exist for pruning connections of a neural network after it was trained without connectivity constraints.
We present an algorithm, DEEP R, that enables us to train directly a sparsely connected neural network.
DEEP R automatically rewires the network during supervised training so that connections are there where they are most needed for the task, while its total number is all the time strictly bounded.
We demonstrate that DEEP R can be used to train very sparse feedforward and recurrent neural networks on standard benchmark tasks with just a minor loss in performance.
DEEP R is based on a rigorous theoretical foundation that views rewiring as stochastic sampling of network configurations from a posterior.

assistant: text: The authors provide a simple algorithm capable of training with limited memory
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: The Singular Values of Convolutional Layers

Paper content:
We characterize the singular values of the linear transformation associated with a standard 2D multi-channel convolutional layer, enabling their efficient computation.
This characterization also leads to an algorithm for projecting a convolutional layer onto an operator-norm ball.
We show that this is an effective regularizer;  for example, it improves the test error of a deep residual network using batch normalization on CIFAR-10 from 6.2% to 5.3%.

assistant: text: Derives exact formulas for computing singular values of convolutional layers of deep neural networks and show that computing the singular values can be done much faster than computing the full SVD of the convolution matrix by appealing to fast FFT transformations.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Varieties of Explainable Agency

Paper content:
In this paper, I discuss some varieties of explanation that can arise
in intelligent agents.
I distinguish between process accounts, which
address the detailed decisions made during heuristic search, and
preference accounts, which clarify the ordering of alternatives
independent of how they were generated.
I also hypothesize
which types of users will appreciate which types of explanation.
In addition, I discuss three facets of multi-step decision making
-- conceptual inference, plan generation, and plan execution --
in which explanations can arise.
I also consider alternative ways
to present questions to agents and for them provide their answers.

assistant: text: This position paper analyzes different types of self explanation that can arise in planning and related systems.
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: Our work applies meta-learning to multi-agent Reinforcement Learning to help our agent efficiently adapted to new coming opponents.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Collapse of deep and narrow neural nets

Paper content:
Recent theoretical work has demonstrated that deep neural networks have superior performance over shallow networks, but their training is more difficult, e.g., they suffer from the vanishing gradient problem.
This problem can be typically resolved by the rectified linear unit (ReLU) activation.
However, here we show that even for such activation, deep and narrow neural networks (NNs) will converge to erroneous mean or median states of the target function depending on the loss with high probability.
Deep and narrow NNs are encountered in solving partial differential equations with high-order derivatives.
We demonstrate this collapse of such NNs both numerically and theoretically, and provide estimates of the probability of collapse.
We also construct a diagram of a safe region for designing NNs that avoid the collapse to erroneous states.
Finally, we examine different ways of initialization and normalization that may avoid the collapse problem.
Asymmetric initializations may reduce the probability of collapse but do not totally eliminate it.

assistant: text: This paper studies failure modes of deep and narrow networks, focusing on as small as possible models for which the undesired behavior occurs.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Deep Rewiring: Training very sparse deep networks

Paper content:
Neuromorphic hardware tends to pose limits on the connectivity of deep networks that one can run on them.
But also generic hardware and software implementations of deep learning run more efficiently for sparse networks.
Several methods exist for pruning connections of a neural network after it was trained without connectivity constraints.
We present an algorithm, DEEP R, that enables us to train directly a sparsely connected neural network.
DEEP R automatically rewires the network during supervised training so that connections are there where they are most needed for the task, while its total number is all the time strictly bounded.
We demonstrate that DEEP R can be used to train very sparse feedforward and recurrent neural networks on standard benchmark tasks with just a minor loss in performance.
DEEP R is based on a rigorous theoretical foundation that views rewiring as stochastic sampling of network configurations from a posterior.

assistant: text: An approach to implement deep learning directly on sparsely connected graphs, allowing networks to be trained efficiently online and for fast and flexible learning.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Morpho-MNIST: Quantitative Assessment and Diagnostics for Representation Learning

Paper content:
Revealing latent structure in data is an active field of research, having introduced exciting technologies such as variational autoencoders and adversarial networks, and is essential to push machine learning towards unsupervised knowledge discovery.
However, a major challenge is the lack of suitable benchmarks for an objective and quantitative evaluation of learned representations.
To address this issue we introduce Morpho-MNIST, a framework that aims to answer: "to what extent has my model learned to represent specific factors of variation in the data?"
We extend the popular MNIST dataset by adding a morphometric analysis enabling quantitative comparison of trained models, identification of the roles of latent variables, and characterisation of sample diversity.
We further propose a set of quantifiable perturbations to assess the performance of unsupervised and supervised methods on challenging tasks such as outlier detection and domain adaptation.

assistant: text: This paper discusses the problem of evaluating and diagnosing the represenatations learnt using a generative model.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Reproducibility and Stability Analysis in Metric-Based Few-Shot Learning

Paper content:
We propose a study of the stability of several few-shot learning algorithms subject to variations in the hyper-parameters and optimization schemes while controlling the random seed.
We propose a methodology for testing for statistical differences in model performances under several replications.
To study this specific design, we attempt to reproduce results from three prominent papers: Matching Nets, Prototypical Networks, and TADAM.
We analyze on the miniImagenet dataset on the standard classification task in the 5-ways, 5-shots learning setting at test time.
We find that the selected implementations exhibit stability across random seed, and repeats.

assistant: text: This paper studies reproducibility for few-shot learning.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Influence-Directed Explanations for Deep Convolutional Networks

Paper content:
We study the problem of explaining a rich class of behavioral properties of deep neural networks.
Our influence-directed explanations approach this problem by peering inside the network to identify neurons with high influence on the property of interest using an axiomatically justified influence measure, and then providing an interpretation for the concepts these neurons represent.
We evaluate our approach by training convolutional neural networks on Pubfig, ImageNet, and Diabetic Retinopathy datasets.
Our evaluation demonstrates that influence-directed explanations (1) localize features used by the network, (2) isolate features distinguishing related instances, (3) help extract the essence of what the network learned about the class, and (4) assist in debugging misclassifications.

assistant: text: This paper proposes to measure the influence of single neurons with regard to a quantity of interest represented by another neuron.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Linearizing Visual Processes with Deep Generative Models

Paper content:
This work studies the problem of modeling non-linear visual processes by leveraging deep generative architectures for learning linear, Gaussian models of observed sequences.
We propose a joint learning framework, combining a multivariate autoregressive model and deep convolutional generative networks.
After justification of theoretical assumptions of inearization, we propose an architecture that allows Variational Autoencoders and Generative Adversarial Networks to simultaneously learn the non-linear observation as well as the linear state-transition model from a sequence of observed frames.
Finally, we demonstrate our approach on conceptual toy examples and dynamic textures.

assistant: text: This paper proposes a new deep generative model for sequences, particularly image sequences and video, which uses a linear structure in part of the model.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learnable Higher-order Representation for Action Recognition

Paper content:
Capturing spatiotemporal dynamics is an essential topic in video recognition.
In this paper, we present learnable higher-order operation as a generic family of building blocks for capturing higher-order correlations from high dimensional input video space.
We prove that several successful architectures for visual classification tasks are in the family of higher-order neural networks, theoretical and experimental analysis demonstrates their underlying mechanism is higher-order.
On the task of video recognition, even using RGB only without fine-tuning with other video datasets, our higher-order models can achieve results on par with or better than the existing state-of-the-art methods on both Something-Something (V1 and V2) and Charades datasets.

assistant: text: Proposed higher order operation for context learning
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning to Infer and Execute 3D Shape Programs

Paper content:
Human perception of 3D shapes goes beyond reconstructing them as a set of points or a composition of geometric primitives: we also effortlessly understand higher-level shape structure such as the repetition and reflective symmetry of object parts.
In contrast, recent advances in 3D shape sensing focus more on low-level geometry but less on these higher-level relationships.
In this paper, we propose 3D shape programs, integrating bottom-up recognition systems with top-down, symbolic program structure to capture both low-level geometry and high-level structural priors for 3D shapes.
Because there are no annotations of shape programs for real shapes, we develop neural modules that not only learn to infer 3D shape programs from raw, unannotated shapes, but also to execute these programs for shape reconstruction.
After initial bootstrapping, our end-to-end differentiable model learns 3D shape programs by reconstructing shapes in a self-supervised manner.
Experiments demonstrate that our model accurately infers and executes 3D shape programs for highly complex shapes from various categories.
It can also be integrated with an image-to-shape module to infer 3D shape programs directly from an RGB image, leading to 3D shape reconstructions that are both more accurate and more physically plausible.

assistant: text: This paper introduces a high-level semantic description for 3D shapes, given by the ShapeProgram.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Characterizing Sparse Connectivity Patterns in Neural Networks

Paper content:
We propose a novel way of reducing the number of parameters in the storage-hungry fully connected layers of a neural network by using pre-defined sparsity, where the majority of connections are absent prior to starting training.
Our results indicate that convolutional neural networks can operate without any loss of accuracy at less than 0.5% classification layer connection density, or less than 5% overall network connection density.
We also investigate the effects of pre-defining the sparsity of networks with only fully connected layers.
Based on our sparsifying technique, we introduce the `scatter' metric to characterize the quality of a particular connection pattern.
As proof of concept, we show results on CIFAR, MNIST and a new dataset on classifying Morse code symbols, which highlights some interesting trends and limits of sparse connection patterns.

assistant: text: Neural networks can be pre-defined to have sparse connectivity without performance degradation.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Gaggle: Visual Analytics for Model Space Navigation

Paper content:
Recent visual analytics systems make use of multiple machine learning models to better fit the data as opposed to traditional single, pre-defined model systems.
However, while multi-model visual analytic systems can be effective, their added complexity poses usability concerns, as users are required to interact with the parameters of multiple models.
Further, the advent of various model algorithms and associated hyperparameters creates an exhaustive model space to sample models from.
This poses complexity to navigate this model space to find the right model for the data and the task.
In this paper, we present Gaggle, a multi-model visual analytic system that enables users to interactively navigate the model space.
Further translating user interactions into inferences, Gaggle simplifies working with multiple models by automatically finding the best model from the high-dimensional model space to support various user tasks.
Through a qualitative user study, we show how our approach helps users to find a best model for a classification and ranking task.
The study results confirm that Gaggle is intuitive and easy to use, supporting interactive model space navigation and automated model selection without requiring any technical expertise from users.

assistant: text: A visual analytics system that helps novice analysts navigate model space in performing classification and ranking tasks.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: DRASIC: Distributed Recurrent Autoencoder for Scalable Image Compression

Paper content:
We propose a new architecture for distributed image compression from a group of distributed data sources.
The work is motivated by practical needs of data-driven codec design, low power consumption, robustness, and data privacy.
The proposed architecture, which we refer to as Distributed Recurrent Autoencoder for Scalable Image Compression (DRASIC), is able to train distributed encoders and one joint decoder on correlated data sources.
Its compression capability is much better than the method of training codecs separately.
Meanwhile, for 10 distributed sources, our distributed system remarkably performs within 2 dB peak signal-to-noise ratio (PSNR) of that of a single codec trained with all data sources.
We experiment distributed sources with different correlations and show how our methodology well matches the Slepian-Wolf Theorem in Distributed Source Coding (DSC).
Our method is also shown to be robust to the lack of presence of encoded data from a number of distributed sources.
Moreover, it is scalable in the sense that codes can be decoded simultaneously at more than one compression quality level.
To the best of our knowledge, this is the first data-driven DSC framework for general distributed code design with deep learning.

assistant: text: We introduce a data-driven Distributed Source Coding framework based on Distributed Recurrent Autoencoder for Scalable Image Compression (DRASIC).
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Few-Shot Regression via Learned Basis Functions

Paper content:
The recent rise in popularity of few-shot learning algorithms has enabled models to quickly adapt to new tasks based on only a few training samples.
Previous few-shot learning works have mainly focused on classification and reinforcement learning.
In this paper, we propose a few-shot meta-learning system that focuses exclusively on regression tasks.
Our model is based on the idea that the degree of freedom of the unknown function can be significantly reduced if it is represented as a linear combination of a set of appropriate basis functions.
This enables a few labelled samples to approximate the function.
We design a Feature Extractor network to encode basis functions for a task distribution, and a  Weights Generator to generate the weight vector for a novel task.
We show that our model outperforms the current state of the art meta-learning methods in various regression tasks.
Regression deals with the problem of learning a model relating a set of inputs to a set of outputs.
The learned model can be thought as function y = F (x) that will give a prediction y ∈ R dy given input x ∈ R dx where d y and d x are dimensions of the output and input respectively.
Typically, a regression model is trained on a large number of data points to be able to provide accurate predictions of new inputs.
Recently, there have been a surge in popularity on few-shot learning methods (Vinyals et al., 2016; BID7 BID4 .
Few-shot learning methods require only a few examples from each task to be able to quickly adapt and perform well on a new task.
The fewshot learning model in essence is learning to learn i.e. the model learns to quickly adapt itself to new tasks rather than just learning to give the correct prediction for a particular input sample.In this work, we propose a few shot learning model that targets few-shot regression tasks.
We evaluate our model on the sinusoidal regression tasks and compare our model's performance to several meta-learning algorithms.
We further introduce two more regression tasks, namely the 1D heat equation task modeled by partial differential equations and the 2D Gaussian distribution task.
We propose a few-shot meta learning system that focuses exclusively on regression tasks.
Our model is based on the idea of linear representation of basis functions.
We design a Feature extractor network to encode the basis functions for the entire task distribution.
We design a Weight generator network to generate the weights from the K training samples of a novel task drawn from the same task distribution.
We show that our model has competitive performance in in various few short regression tasks.
A TECHNICAL DETAILS

assistant: text: We propose a few-shot learning model that is tailored specifically for regression tasks
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Meta-Learning Acquisition Functions for Transfer Learning in Bayesian Optimization

Paper content:
Transferring knowledge across tasks to improve data-efficiency is one of
the open key challenges in the area of global optimization algorithms.
Readily
available algorithms are typically designed to be universal optimizers and, thus,
often suboptimal for specific tasks.
We propose a novel transfer learning method to
obtain customized optimizers within the well-established framework of Bayesian
optimization, allowing our algorithm to utilize the proven generalization
capabilities of Gaussian processes.
Using reinforcement learning to meta-train an
acquisition function (AF) on a set of related tasks, the proposed method learns to
extract implicit structural information and to exploit it for improved data-efficiency.
We present experiments on a sim-to-real transfer task as well as on several simulated
functions and two hyperparameter search problems.
The results show that our
algorithm (1) automatically identifies structural properties of objective functions
from available source tasks or simulations, (2) performs favourably in settings with
both scarse and abundant source data, and (3) falls back to the performance level
of general AFs if no structure is present.

assistant: text: We perform efficient and flexible transfer learning in the framework of Bayesian optimization through meta-learned neural acquisition functions.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Efficient Deep Representation Learning by Adaptive Latent Space Sampling

Paper content:
Supervised deep learning requires a large amount of training samples with annotations (e.g. label class for classification task, pixel- or voxel-wised label map for segmentation tasks), which are expensive and time-consuming to obtain.
During the training of a deep neural network, the annotated samples are fed into the network in a mini-batch way, where they are often regarded of equal importance.
However, some of the samples may become less informative during training, as the magnitude of the gradient start to vanish for these samples.
In the meantime, other samples of higher utility or hardness may be more demanded for the training process to proceed and require more exploitation.
To address the challenges of expensive annotations and loss of sample informativeness, here we propose a novel training framework which adaptively selects informative samples that are fed to the training process.
The adaptive selection or sampling is performed based on a hardness-aware strategy in the latent space constructed by a generative model.
To evaluate the proposed training framework, we perform experiments on three different datasets, including MNIST and CIFAR-10 for image classification task and a medical image dataset IVUS for biophysical simulation task.
On all three datasets, the proposed framework outperforms a random sampling method, which demonstrates the effectiveness of our framework.

assistant: text: This paper introduces a framework for data-efficient representation learning by adaptive sampling in latent space.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Combination of Supervised and Reinforcement Learning For Vision-Based Autonomous Control

Paper content:
Reinforcement learning methods have recently achieved impressive results on a wide range of control problems.
However, especially with complex inputs, they still require an extensive amount of training data in order to converge to a meaningful solution.
This limitation largely prohibits  their usage for complex input spaces such as video signals, and it is still impossible to use it for a number of complex problems in a real world environments, including many of those for video based control.
Supervised learning, on the contrary, is capable of learning on a relatively small number of samples, however it does not take into account reward-based control policies and is not capable to provide independent control policies.
In this article we propose a model-free control method, which uses a combination of reinforcement and supervised learning for autonomous control and paves the way towards policy based control in real world environments.
We use SpeedDreams/TORCS video game to demonstrate that our approach requires much less samples (hundreds of thousands against millions or tens of millions) comparing to the state-of-the-art reinforcement learning techniques on similar data, and at the same time overcomes both supervised and reinforcement learning approaches in terms of quality.
Additionally, we demonstrate the applicability of the method to MuJoCo control problems.

assistant: text: This paper proposes leveraging labelled controlled data to accelerate reinforcement-based learning of a control policy
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Newtonian Monte Carlo: a second-order gradient method for speeding up MCMC

Paper content:
We present Newtonian Monte Carlo (NMC), a method to improve Markov Chain Monte Carlo (MCMC) convergence by analyzing the first and second order gradients of the target density to determine a suitable proposal density at each point.
Existing first order gradient-based methods suffer from the problem of determining an appropriate step size.
Too small a step size and it will take a large number of steps to converge, while a very large step size will cause it to overshoot the high density region.
NMC is similar to the Newton-Raphson update in optimization where the second order gradient is used to automatically scale the step size in each dimension.
However, our objective is not to find a maxima but instead to find a parameterized density that can best match the local curvature of the target density.
This parameterized density is then used as a single-site Metropolis-Hastings proposal.
As a further improvement on first order methods, we show that random variables with constrained supports don't need to be transformed before taking a gradient step.
NMC directly matches constrained random variables to a proposal density with the same support thus keeping the curvature of the target density intact.
We demonstrate the efficiency of NMC on a number of different domains.
For statistical models where the prior is conjugate to the likelihood, our method recovers the posterior quite trivially in one step.
However, we also show results on fairly large non-conjugate models, where NMC performs better than adaptive first order methods such as NUTS or other inexact scalable inference methods such as Stochastic Variational Inference or bootstrapping.

assistant: text: Exploit curvature to make MCMC methods converge faster than state of the art.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Progressive Knowledge Distillation For Generative Modeling

Paper content:
While modern  generative  models are able to synthesize high-fidelity, visually appealing images, successfully generating examples that are useful for recognition tasks remains an elusive goal.
To this end, our key insight is that the examples should be synthesized to recover classifier decision boundaries that would be learned from a large amount of real examples.
More concretely, we treat a classifier trained on synthetic examples as ''student'' and a classifier trained on real examples as ''teacher''.
By introducing knowledge distillation into a meta-learning framework, we encourage the generative model to produce examples in a way that enables the student classifier to mimic the behavior of the teacher.
To mitigate the potential gap between student and teacher classifiers, we further propose to distill the knowledge in a progressive manner, either by gradually strengthening the teacher or weakening the student.
We demonstrate the use of our model-agnostic distillation approach to deal with data scarcity, significantly improving few-shot learning performance on miniImageNet and ImageNet1K benchmarks.

assistant: text: This paper demonstrates easy-to-hard curriculum learning to train a generative model to improve few-shot classification.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Representation Learning with Multisets

Paper content:
We study the problem of learning permutation invariant representations that can capture containment relations.
We propose training a model on a novel task: predicting the size of the symmetric difference between pairs of multisets, sets which may contain multiple copies of the same object.
With motivation from fuzzy set theory, we formulate both multiset representations and how to predict symmetric difference sizes given these representations.
We model multiset elements as vectors on the standard simplex and multisets as the summations of such vectors, and we predict symmetric difference as the l1-distance between multiset representations.
We demonstrate that our representations more effectively predict the sizes of symmetric differences than DeepSets-based approaches with unconstrained object representations.
Furthermore, we demonstrate that the model learns meaningful representations, mapping objects of different classes to different standard basis vectors.

assistant: text: This paper proposes a new task of set learning, predicting the size of the symmetric difference between multisets, and gives a method to solve the task based on fuzzy set theory.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: MMA Training: Direct Input Space Margin Maximization through Adversarial Training

Paper content:
We study adversarial robustness of neural networks from a margin maximization perspective, where margins are defined as the distances from inputs to a classifier's decision boundary.
Our study shows that maximizing margins can be achieved by minimizing the adversarial loss on the decision boundary at the "shortest successful perturbation", demonstrating a close connection between adversarial losses and the margins.
We propose Max-Margin Adversarial (MMA) training to directly maximize the margins to achieve adversarial robustness.
Instead of adversarial training with a fixed $\epsilon$, MMA offers an improvement by enabling adaptive selection of the "correct" $\epsilon$ as the margin individually for each datapoint.
In addition, we rigorously analyze adversarial training with the perspective of margin maximization, and provide an alternative interpretation for adversarial training, maximizing either a lower or an upper bound of the margins.
Our experiments empirically confirm our theory and demonstrate MMA training's efficacy on the MNIST and CIFAR10 datasets w.r.t. $\ell_\infty$ and $\ell_2$ robustness.

assistant: text: We propose MMA training to directly maximize input space margin in order to improve adversarial robustness primarily by removing the requirement of specifying a fixed distortion bound.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Deep Variational Semi-Supervised Novelty Detection

Paper content:
In anomaly detection (AD), one seeks to identify whether a test sample is abnormal,  given a data set of normal samples.
A recent and promising approach to AD relies on deep generative models, such as variational autoencoders (VAEs),for unsupervised learning of the normal data distribution.
In semi-supervised AD (SSAD), the data also includes a small sample of labeled anomalies.
In this work,we propose two variational methods for training VAEs for SSAD.
The intuitive idea in both methods is to train the encoder to ‘separate’ between latent vectors for normal and outlier data.
We show that this idea can be derived from principled probabilistic formulations of the problem, and propose simple and effective algorithms.
Our methods can be applied to various data types, as we demonstrate on SSAD datasets ranging from natural images to astronomy and medicine, and can be combined with any VAE model architecture.
When comparing to state-of-the-art SSAD methods that are not specific to particular data types, we obtain marked improvement in outlier detection.

assistant: text: We proposed two VAE modifications that account for negative data examples, and used them for semi-supervised anomaly detection.