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

Title: 

Paper content:
This work provides theoretical and empirical evidence that invariance-inducing regularizers can increase predictive accuracy for worst-case spatial transformations (spatial robustness).
Evaluated on these adversarially transformed examples, we demonstrate that adding regularization on top of standard or adversarial training reduces the relative error by 20% for CIFAR10 without increasing the computational cost.
This outperforms handcrafted networks that were explicitly designed to be spatial-equivariant.
Furthermore, we observe for SVHN, known to have inherent variance in orientation, that robust training also improves standard accuracy on the test set.

assistant: text: for spatial transformations robust minimizer also minimizes standard accuracy; invariance-inducing regularization leads to better robustness than specialized architectures
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Leveraging Grammar and Reinforcement Learning for Neural Program Synthesis

Paper content:
Program synthesis is the task of automatically generating a program consistent with
a specification.
Recent years have seen proposal of a number of neural approaches
for program synthesis, many of which adopt a sequence generation paradigm similar
to neural machine translation, in which sequence-to-sequence models are trained to
maximize the likelihood of known reference programs.
While achieving impressive
results, this strategy has two key limitations.
First, it ignores Program Aliasing: the
fact that many different programs may satisfy a given specification (especially with
incomplete specifications such as a few input-output examples).
By maximizing
the likelihood of only a single reference program, it penalizes many semantically
correct programs, which can adversely affect the synthesizer performance.
Second,
this strategy overlooks the fact that programs have a strict syntax that can be
efficiently checked.
To address the first limitation, we perform reinforcement
learning on top of a supervised model with an objective that explicitly maximizes
the likelihood of generating semantically correct programs.
For addressing the
second limitation, we introduce a training procedure that directly maximizes the
probability of generating syntactically correct programs that fulfill the specification.
We show that our contributions lead to improved accuracy of the models, especially
in cases where the training data is limited.

assistant: text: Using the DSL grammar and reinforcement learning to improve synthesis of programs with complex control flow.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Discrete Flows: Invertible Generative Models of Discrete Data

Paper content:
While normalizing flows have led to significant advances in modeling high-dimensional continuous distributions, their applicability to discrete distributions remains unknown.
In this paper, we show that flows can in fact be extended to discrete events---and under a simple change-of-variables formula not requiring log-determinant-Jacobian computations.
Discrete flows have numerous applications.
We display proofs of concept under 2 flow architectures: discrete autoregressive flows enable bidirectionality, allowing for example tokens in text to depend on both left-to-right and right-to-left contexts in an exact language model; and discrete bipartite flows (i.e., with layer structure from RealNVP) enable parallel generation such as exact nonautoregressive text modeling.
There have been many recent advances in normalizing flows, a technique for constructing high-dimensional continuous distributions from invertible transformations of simple distributions BID22 BID25 BID23 .
Applications for high-dimensional continuous distributions are widespread: these include latent variable models with expressive posterior approximations BID22 BID20 BID12 , parallel image generation BID6 BID11 , parallel speech synthesis BID19 , and general-purpose density estimation BID18 .Normalizing
flows are based on the change-of-variables formula, which derives a density given an invertible function applied to continuous events. There have
not been analogous advances for discrete distributions, where flows are typically thought to not be applicable. Instead, most
research for discrete data has focused on building either latent-variable models with approximate inference BID2 , or increasingly sophisticated autoregressive models that assume a fixed ordering of the data BID0 BID26 . In this paper
, we present an alternative for flexible modeling of discrete sequences by extending continuous normalizing flows to the discrete setting. We demonstrate
proofs of concept of discrete flows with two architectures:1. Discrete autoregressive
flows enable multiple levels of autoregressivity. For example, one can design
a bidirectional language model of text where each token depends on both left-to-right and right-to-left contexts while maintaining an exact likelihood and sampling.2. Discrete bipartite flows (i.e.
, with flow structure similar to RealNVP BID6 ) enable flexible models with parallel generation. For example, one can design nonautoregressive
text models which maintain an exact likelihood for training and evaluation.

assistant: text: We extend autoregressive flows and RealNVP to discrete data.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: SCL: Towards Accurate Domain Adaptive Object Detection via Gradient Detach Based Stacked Complementary Losses

Paper content:
Unsupervised domain adaptive object detection aims to learn a robust detector on the domain shift circumstance, where the training (source) domain is label-rich with bounding box annotations, while the testing (target) domain is label-agnostic and the feature distributions between training and testing domains are dissimilar or even totally different.
In this paper, we propose a gradient detach based Stacked Complementary Losses (SCL) method that uses detection objective (cross entropy and smooth l1 regression) as the primary objective, and cuts in several auxiliary losses in different network stages to utilize information from the complement data (target images) that can be effective in adapting model parameters to both source and target domains.
A gradient detach operation is applied between detection and context sub-networks during training to force networks to learn discriminative representations.
We argue that the conventional training with primary objective mainly leverages the information from the source-domain for maximizing likelihood and ignores the complement data in shallow layers of networks, which leads to an insufficient integration within different domains.
Thus, our proposed method is a more syncretic adaptation learning process.
We conduct comprehensive experiments on seven datasets, the results demonstrate that our method performs favorably better than the state-of-the-art methods by a large margin.
For instance, from Cityscapes to FoggyCityscapes, we achieve 37.9% mAP, outperforming the previous art Strong-Weak by 3.6%.

assistant: text: We introduce a new gradient detach based complementary objective training strategy for domain adaptive object detection.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Pipelined Training with Stale Weights of Deep Convolutional Neural Networks

Paper content:
The growth in the complexity of Convolutional Neural Networks (CNNs) is increasing interest in partitioning a network across multiple accelerators during training and pipelining the backpropagation computations over the accelerators.
Existing approaches avoid or limit the use of stale weights through techniques such as micro-batching or weight stashing.
These techniques either underutilize of accelerators or increase memory footprint.
We explore the impact of stale weights on the statistical efficiency and performance in a pipelined backpropagation scheme that maximizes accelerator utilization and keeps memory overhead modest.
We use 4 CNNs (LeNet-5, AlexNet, VGG and ResNet) and show that when pipelining is limited to early layers in a network, training with stale weights converges and results in models with comparable inference accuracies to those resulting from non-pipelined training on MNIST and CIFAR-10 datasets; a drop in accuracy of 0.4%, 4%, 0.83% and 1.45% for the 4 networks, respectively.
However, when pipelining is deeper in the network, inference accuracies drop significantly.
We propose combining pipelined and non-pipelined training in a hybrid scheme to address this drop.
We demonstrate the implementation and performance of our pipelined backpropagation in PyTorch on 2 GPUs using ResNet, achieving speedups of up to 1.8X over a 1-GPU baseline, with a small drop in inference accuracy.

assistant: text: Accelerating CNN training on a Pipeline of Accelerators with Stale Weights
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: SloMo: Improving Communication-Efficient Distributed SGD with Slow Momentum

Paper content:
Distributed optimization is essential for training large models on large datasets.
Multiple approaches have been proposed to reduce the communication overhead in distributed training, such as synchronizing only after performing multiple local SGD steps, and decentralized methods (e.g., using gossip algorithms) to decouple communications among workers.
Although these methods run faster than AllReduce-based methods, which use blocking communication before every update, the resulting models may be less accurate after the same number of updates.
Inspired by the BMUF method of Chen & Huo (2016), we propose a slow momentum (SloMo) framework, where workers periodically synchronize and perform a momentum update, after multiple iterations of a base optimization algorithm.
Experiments on image classification and machine translation tasks demonstrate that SloMo consistently yields improvements in optimization and generalization performance relative to the base optimizer, even when the additional overhead is amortized over many updates so that the SloMo runtime is on par with that of the base optimizer.
We provide theoretical convergence guarantees showing that SloMo converges to a stationary point of smooth non-convex losses.
Since BMUF is a particular instance of the SloMo framework, our results also correspond to the first theoretical convergence guarantees for BMUF.

assistant: text: SlowMo improves the optimization and generalization performance of communication-efficient decentralized algorithms without sacrificing speed.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Stochastic Geodesic Optimization for Neural Networks

Paper content:
We develop a novel and efficient algorithm for optimizing neural networks inspired by a recently proposed geodesic optimization algorithm.
Our algorithm, which we call Stochastic  Geodesic Optimization (SGeO), utilizes an adaptive coefficient on top of Polyak's Heavy Ball method effectively controlling the amount of weight put on the previous update to the parameters based on the change of direction in the optimization path.
Experimental results on strongly convex functions with Lipschitz gradients and deep Autoencoder benchmarks show that SGeO reaches lower errors than established first-order methods and competes well with lower or similar errors to a recent second-order method called K-FAC (Kronecker-Factored Approximate Curvature).
We also incorporate Nesterov style lookahead gradient into our algorithm (SGeO-N) and observe notable improvements.

assistant: text: We utilize an adaptive coefficient on top of regular momentum inspired by geodesic optimization which significantly speeds up training in both convex and non-convex functions.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Sliced-Wasserstein Autoencoder: An Embarrassingly Simple Generative Model

Paper content:
In this paper we study generative modeling via autoencoders while using the elegant geometric properties of the optimal transport (OT) problem and the Wasserstein distances.
We introduce Sliced-Wasserstein Autoencoders (SWAE), which are generative models that enable one to shape the distribution of the latent space into any samplable probability distribution without the need for training an adversarial network or defining a closed-form for the distribution.
In short, we regularize the autoencoder loss with the sliced-Wasserstein distance between the distribution of the encoded training samples and a predefined samplable distribution.
We show that the proposed formulation has an efficient numerical solution that provides similar capabilities to Wasserstein Autoencoders (WAE) and Variational Autoencoders (VAE), while benefiting from an embarrassingly simple implementation.

assistant: text: "Generative modeling with no need for adversarial training"
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Improving the Generalization of Visual Navigation Policies using Invariance Regularization

Paper content:
Training agents to operate in one environment often yields overfitted models that are unable to generalize to the changes in that environment.
However, due to the numerous variations that can occur in the real-world, the agent is often required to be robust in order to be useful.
This has not been the case for agents trained with reinforcement learning (RL) algorithms.
In this paper, we investigate the overfitting of RL agents to the training environments in visual navigation tasks.
Our experiments show that deep RL agents can overfit even when trained on multiple environments simultaneously.
We propose a regularization method which combines RL with supervised learning methods by adding a term to the RL objective that would encourage the invariance of a policy to variations in the observations that ought not to affect the action taken.
The results of this method, called invariance regularization, show an improvement in the generalization of policies to environments not seen during training.

assistant: text: We propose a regularization term that, when added to the reinforcement learning objective, allows the policy to maximize the reward and simultaneously learn to be invariant to the irrelevant changes within the input..
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Stabilizing Adversarial Nets with Prediction Methods

Paper content:
Adversarial neural networks solve many important problems in data science, but are notoriously difficult to train.
These difficulties come from the fact that optimal weights for adversarial nets correspond to saddle points, and not minimizers, of the loss function.
The alternating stochastic gradient methods typically used for such problems do not reliably converge to saddle points, and when convergence does happen it is often highly sensitive to learning rates.
We propose a simple modification of stochastic gradient descent that stabilizes adversarial networks.
We show, both in theory and practice, that the proposed method reliably converges to saddle points.
This makes adversarial networks less likely to "collapse," and enables faster training with larger learning rates.

assistant: text: We present a simple modification to the alternating SGD method, called a prediction step, that improves the stability of adversarial networks.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: The Convex Information Bottleneck Lagrangian

Paper content:
The information bottleneck (IB) problem tackles the issue of obtaining relevant compressed representations T of some random variable X for the task of predicting Y. It is defined as a constrained optimization problem which maximizes the information the representation has about the task, I(T;Y), while ensuring that a minimum level of compression r is achieved (i.e., I(X;T) <= r).
For practical reasons the problem is usually solved by maximizing the IB Lagrangian for many values of the Lagrange multiplier, therefore drawing the IB curve (i.e., the curve of maximal I(T;Y) for a given I(X;Y)) and selecting the representation of desired predictability and compression.
It is known when Y is a deterministic function of X, the IB curve cannot be explored and other Lagrangians have been proposed to tackle this problem (e.g., the squared IB Lagrangian).
In this paper we
(i) present a general family of Lagrangians which allow for the exploration of the IB curve in all scenarios;
(ii) prove that if these Lagrangians are used, there is a one-to-one mapping between the Lagrange multiplier and the desired compression rate r for known IB curve shapes, hence, freeing from the burden of solving the optimization problem for many values of the Lagrange multiplier.

assistant: text: We introduce a general family of Lagrangians that allow exploring the IB curve in all scenarios. When these are used, and the IB curve is known, one can optimize directly for a performance/compression level directly.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Mint: Matrix-Interleaving for Multi-Task Learning

Paper content:
Deep learning enables training of large and flexible function approximators from scratch at the cost of large amounts of data.
Applications of neural networks often consider learning in the context of a single task.
However, in many scenarios what we hope to learn is not just a single task, but a model that can be used to solve multiple different tasks.
Such multi-task learning settings have the potential to improve data efficiency and generalization by sharing data and representations across tasks.
However, in some challenging multi-task learning settings, particularly in reinforcement learning, it is very difficult to learn a single model that can solve all the tasks while realizing data efficiency and performance benefits.
Learning each of the tasks independently from scratch can actually perform better in such settings, but it does not benefit from the representation sharing that multi-task learning can potentially provide.
In this work, we develop an approach that endows a single model with the ability to represent both extremes: joint training and independent training.
To this end, we introduce matrix-interleaving (Mint), a modification to standard neural network models that projects the activations for each task into a different learned subspace, represented by a per-task and per-layer matrix.
By learning these matrices jointly with the other model parameters, the optimizer itself can decide how much to share representations between tasks.
On three challenging multi-task supervised learning and reinforcement learning problems with varying degrees of shared task structure, we find that this model consistently matches or outperforms joint training and independent training, combining the best elements of both.

assistant: text: We propose an approach that endows a single model with the ability to represent both extremes: joint training and independent training, which leads to effective multi-task learning.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: What Is in a Translation Unit?  Comparing Character and Subword Representations Beyond Translation

Paper content:
Recent work has shown that contextualized word representations derived from neural machine translation (NMT) are a viable alternative to such from simple word predictions tasks.
This is because the internal understanding that needs to be built in order to be able to translate from one language to another is much more comprehensive.
Unfortunately, computational and memory limitations as of present prevent NMT models from using large word vocabularies, and thus alternatives such as subword units (BPE and morphological segmentations) and characters have been used.
Here we study the impact of using different kinds of units on the quality of the resulting representations when used to model syntax, semantics, and morphology.
We found that while representations derived from subwords are slightly better for modeling syntax, character-based representations are superior for modeling morphology and are also more robust to noisy input.

assistant: text: We study the impact of using different kinds of subword units on the quality of the resulting representations when used to model syntax, semantics, and morphology.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Incremental Learning of Action Models for Planning

Paper content:
The soundness and optimality of a plan depends on the correctness of the domain model.
In real-world applications, specifying complete domain models is difficult as the interactions between the agent and its environment can be quite complex.
We propose a framework to learn a PPDDL representation of the model incrementally over multiple planning problems using only experiences from the current planning problem, which suits non-stationary environments.
We introduce the novel concept of reliability as an intrinsic motivation for reinforcement learning, and as a means of learning from failure to prevent repeated instances of similar failures.
Our motivation is to improve both learning efficiency and goal-directedness.
We evaluate our work with experimental results for three planning domains.

assistant: text: Introduce an approach to allow agents to learn  PPDDL action models incrementally over multiple planning problems under the framework of reinforcement learning.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: SMASH: One-Shot Model Architecture Search through HyperNetworks

Paper content:
Designing architectures for deep neural networks requires expert knowledge and substantial computation time.
We propose a technique to accelerate architecture selection by learning an auxiliary HyperNet that generates the weights of a main model conditioned on that model's architecture.
By comparing the relative validation performance of networks with HyperNet-generated weights, we can effectively search over a wide range of architectures at the cost of a single training run.
To facilitate this search, we develop a flexible mechanism based on memory read-writes that allows us to define a wide range of network connectivity patterns, with ResNet, DenseNet, and FractalNet blocks as special cases.
We validate our method (SMASH) on CIFAR-10 and CIFAR-100, STL-10, ModelNet10, and Imagenet32x32, achieving competitive performance with similarly-sized hand-designed networks.

assistant: text: A technique for accelerating neural architecture selection by approximating the weights of each candidate architecture instead of training them individually.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: RNA Secondary Structure Prediction By Learning Unrolled Algorithms

Paper content:
In this paper, we propose an end-to-end deep learning model, called E2Efold, for RNA secondary structure prediction which can effectively take into account the inherent constraints in the problem.
The key idea of E2Efold is to directly predict the RNA base-pairing matrix, and use an unrolled constrained programming algorithm as a building block in the architecture to enforce constraints.
With comprehensive experiments on benchmark datasets, we demonstrate the superior performance of E2Efold: it predicts significantly better structures compared to previous SOTA (29.7% improvement in some cases in F1 scores and even larger improvement for pseudoknotted structures) and runs as efficient as the fastest algorithms in terms of inference time.

assistant: text: A DL model for RNA secondary structure prediction, which uses an unrolled algorithm in the architecture to enforce constraints.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Tree-structured Attention Module for Image Classification

Paper content:
Recent studies in attention modules have enabled higher performance in computer vision tasks by capturing global contexts and accordingly attending important features.
In this paper, we propose a simple and highly parametrically efficient module named Tree-structured Attention Module (TAM) which recursively encourages neighboring channels to collaborate in order to produce a spatial attention map as an output.
Unlike other attention modules which try to capture long-range dependencies at each channel, our module focuses on imposing non-linearities be- tween channels by utilizing point-wise group convolution.
This module not only strengthens representational power of a model but also acts as a gate which controls signal flow.
Our module allows a model to achieve higher performance in a highly parameter-efficient manner.
We empirically validate the effectiveness of our module with extensive experiments on CIFAR-10/100 and SVHN datasets.
With our proposed attention module employed, ResNet50 and ResNet101 models gain 2.3% and 1.2% accuracy improvement with less than 1.5% parameter over- head.
Our PyTorch implementation code is publicly available.

assistant: text: Our paper proposes an attention module which captures inter-channel relationships and offers large performance gains.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Situating Sentence Embedders with Nearest Neighbor Overlap

Paper content:
As distributed approaches to natural language semantics have developed and diversified, embedders for linguistic units larger than words (e.g., sentences) have come to play an increasingly important role.
To date, such embedders have been evaluated using benchmark tasks (e.g., GLUE) and linguistic probes.
We propose a comparative approach, nearest neighbor overlap (N2O), that quantifies similarity between embedders in a task-agnostic manner.
N2O requires only a collection of examples and is simple to understand: two embedders are more similar if, for the same set of inputs, there is greater overlap between the inputs' nearest neighbors.
We use N2O to compare 21 sentence embedders and show the effects of different design choices and architectures.

assistant: text: We propose nearest neighbor overlap, a procedure which quantifies similarity between embedders in a task-agnostic manner, and use it to compare 21 sentence embedders.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Compressed Sensing and Overparametrized Networks: Overfitting Peaks in a Model of Misparametrized Sparse Regression in the Interpolation Limit

Paper content:
Current practice in machine learning is to employ deep nets in an overparametrized limit, with the nominal number of parameters typically exceeding the number of measurements.
This resembles the situation in compressed sensing, or in sparse regression with $l_1$ penalty terms, and provides a theoretical avenue for understanding phenomena that arise in the context of deep nets.
One such phenonemon is the success of deep nets in providing good generalization in an interpolating regime with zero training error.
Traditional statistical practice calls for regularization or smoothing to prevent "overfitting" (poor generalization performance).
However, recent work shows that there exist data interpolation procedures which are statistically consistent and provide good generalization performance\cite{belkin2018overfitting} ("perfect fitting").
In this context, it has been suggested that "classical" and "modern" regimes for machine learning are separated by a peak in the generalization error ("risk") curve, a phenomenon dubbed "double descent"\cite{belkin2019reconciling}.
While such overfitting peaks do exist and arise from ill-conditioned design matrices, here we challenge the interpretation of the overfitting peak as demarcating the regime where good generalization occurs under overparametrization.
We propose a model of Misparamatrized Sparse Regression (MiSpaR) and analytically compute the GE curves for $l_2$ and $l_1$ penalties.
We show that the overfitting peak arising in the interpolation limit is dissociated from the regime of good generalization.
The analytical expressions are obtained in the so called "thermodynamic" limit.
We find an additional interesting phenomenon: increasing overparametrization in the fitting model increases sparsity, which should intuitively improve performance of $l_1$ penalized regression.
However, at the same time, the relative number of measurements decrease compared to the number of fitting parameters, and eventually overparametrization does lead to poor generalization.
Nevertheless, $l_1$ penalized regression can show good generalization performance under conditions of data interpolation even with a large amount of overparametrization.
These results provide a theoretical avenue into studying inverse problems in the interpolating regime using overparametrized fitting functions such as deep nets.
Modern machine learning has two salient characteristics: large numbers of measurements m, and non-linear parametric models with very many fitting parameters p, with both m and p in the range of 10 6

assistant: text: Proposes an analytically tractable model and inference procedure (misparametrized sparse regression, inferred using L_1 penalty and studied in the data-interpolation limit) to study deep-net related phenomena in the context of inverse problems.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: PRUNING WITH HINTS: AN EFFICIENT FRAMEWORK FOR MODEL ACCELERATION

Paper content:
In this paper, we propose an efficient framework to accelerate convolutional neural networks.
We utilize two types of acceleration methods: pruning and hints.
Pruning can reduce model size by removing channels of layers.
Hints can improve the performance of student model by transferring knowledge from teacher model.
We demonstrate that pruning and hints are complementary to each other.
On one hand, hints can benefit pruning by maintaining similar feature representations.
On the other hand, the model pruned from teacher networks is a good initialization for student model, which increases the transferability between two networks.
Our approach performs pruning stage and hints stage iteratively to further improve the
performance.
Furthermore, we propose an algorithm to reconstruct the parameters of hints layer and make the pruned model more suitable for hints.
Experiments were conducted on various tasks including classification and pose estimation.
Results on CIFAR-10, ImageNet and COCO demonstrate the generalization and superiority of our framework.

assistant: text: This is a work aiming for boosting all the existing pruning and mimic method.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Spatially Transformed Adversarial Examples

Paper content:
Recent studies show that widely used Deep neural networks (DNNs) are vulnerable to the carefully crafted adversarial examples.
Many advanced algorithms have been proposed to generate adversarial examples by leveraging the L_p distance for penalizing perturbations.
Different defense methods have also been explored to defend against such adversarial attacks.
While the effectiveness of L_p distance as a metric of perceptual quality remains an active research area, in this paper we will instead focus on a different type of perturbation, namely spatial transformation, as opposed to manipulating the pixel values directly as in prior works.
Perturbations generated through spatial transformation could result in large L_p distance measures, but our extensive experiments show that such spatially transformed adversarial examples are perceptually realistic and more difficult to defend against with existing defense systems.
This potentially provides a new direction in adversarial example generation and the design of corresponding defenses.
We visualize the spatial transformation based perturbation for different examples and show that our technique
can produce realistic adversarial examples with smooth image deformation.
Finally, we visualize the attention of deep networks with different types of adversarial examples to better understand how these examples are interpreted.

assistant: text: We propose a new approach for generating adversarial examples based on spatial transformation, which produces perceptually realistic examples compared to existing attacks.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Deep Randomized Least Squares Value Iteration

Paper content:
Exploration while learning representations is one of the main challenges Deep
Reinforcement Learning (DRL) faces today.
As the learned representation is dependant in the observed data, the exploration strategy has a crucial role.
The popular DQN algorithm has improved significantly the capabilities of Reinforcement
Learning (RL) algorithms to learn state representations from raw data, yet, it uses
a naive exploration strategy which is statistically inefficient.
The Randomized
Least Squares Value Iteration (RLSVI) algorithm (Osband et al., 2016), on the
other hand, explores and generalizes efficiently via linearly parameterized value
functions.
However, it is based on hand-designed state representation that requires
prior engineering work for every environment.
In this paper, we propose a Deep
Learning adaptation for RLSVI.
Rather than using hand-design state representation, we use a state representation that is being learned directly from the data by a
DQN agent.
As the representation is being optimized during the learning process,
a key component for the suggested method is a likelihood matching mechanism,
which adapts to the changing representations.
We demonstrate the importance of
the various properties of our algorithm on a toy problem and show that our method
outperforms DQN in five Atari benchmarks, reaching competitive results with the
Rainbow algorithm.

assistant: text: A Deep Learning adaptation of Randomized Least Squares Value Iteration
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Meta-Learning Update Rules for Unsupervised Representation Learning

Paper content:
A major goal of unsupervised learning is to discover data representations that are useful for subsequent tasks, without access to supervised labels during training.
Typically, this involves minimizing a surrogate objective, such as the negative log likelihood of a generative model, with the hope that representations useful for subsequent tasks will arise as a side effect.
In this work, we propose instead to directly target later desired tasks by meta-learning an unsupervised learning rule which leads to representations useful for those tasks.
Specifically, we target semi-supervised classification performance, and we meta-learn an algorithm -- an unsupervised weight update rule -- that produces representations useful for this task.
Additionally, we constrain our unsupervised update rule to a be a biologically-motivated, neuron-local function, which enables it to generalize to different neural network architectures, datasets, and data modalities.
We show that the meta-learned update rule produces useful features and sometimes outperforms existing unsupervised learning techniques.
We further show that the meta-learned unsupervised update rule generalizes to train networks with different widths, depths, and nonlinearities.
It also generalizes to train on data with randomly permuted input dimensions and even generalizes from image datasets to a text task.

assistant: text: We learn an unsupervised learning algorithm that produces useful representations from a set of supervised tasks. At test-time, we apply this algorithm to new tasks without any supervision and show performance comparable to a VAE.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Data for free: Fewer-shot algorithm learning with parametricity data augmentation

Paper content:
We address the problem of teaching an RNN to approximate list-processing algorithms given a small number of input-output training examples.
Our approach is to generalize the idea of parametricity from programming language theory to formulate a semantic property that distinguishes common algorithms from arbitrary non-algorithmic functions.
This characterization leads naturally to a learned data augmentation scheme that encourages RNNs to learn algorithmic behavior and enables small-sample learning in a variety of list-processing tasks.

assistant: text: Learned data augmentation instills algorithm-favoring inductive biases that let RNNs learn list-processing algorithms from fewer examples.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Deep Imitative Models for Flexible Inference, Planning, and Control

Paper content:
Imitation Learning (IL) is an appealing approach to learn desirable autonomous behavior.
However, directing IL to achieve arbitrary goals is difficult.
In contrast, planning-based algorithms use dynamics models and reward functions to achieve goals.
Yet, reward functions that evoke desirable behavior are often difficult to specify.
In this paper, we propose "Imitative Models" to combine the benefits of IL and goal-directed planning.
Imitative Models are probabilistic predictive models of desirable behavior able to plan interpretable expert-like trajectories to achieve specified goals.
We derive families of flexible goal objectives, including constrained goal regions, unconstrained goal sets, and energy-based goals.
We show that our method can use these objectives to successfully direct behavior.
Our method substantially outperforms six IL approaches and a planning-based approach in a dynamic simulated autonomous driving task, and is efficiently learned from expert demonstrations without online data collection.
We also show our approach is robust to poorly-specified goals, such as goals on the wrong side of the road.

assistant: text: In this paper, we propose Imitative Models to combine the benefits of IL and goal-directed planning: probabilistic predictive models of desirable behavior able to plan interpretable expert-like trajectories to achieve specified goals.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Prediction, Consistency, Curvature: Representation Learning for Locally-Linear Control

Paper content:
Many real-world sequential decision-making problems can be formulated as optimal control with high-dimensional observations and unknown dynamics.
A promising approach is to embed the high-dimensional observations into a lower-dimensional latent representation space, estimate the latent dynamics model, then utilize this model for control in the latent space.
An important open question is how to learn a representation that is amenable to existing control algorithms?
In this paper, we focus on learning representations for locally-linear control algorithms, such as iterative LQR (iLQR).
By formulating and analyzing the representation learning problem from an optimal control perspective, we establish three underlying principles that the learned representation should comprise: 1) accurate prediction in the observation space, 2) consistency between latent and observation space dynamics, and 3) low curvature in the latent space transitions.
These principles naturally correspond to a loss function that consists of three terms: prediction, consistency, and curvature (PCC).
Crucially, to make PCC tractable, we derive an amortized variational bound for the PCC loss function.
Extensive experiments on benchmark domains demonstrate that the new variational-PCC learning algorithm benefits from significantly more stable and reproducible training, and leads to superior control performance.
Further ablation studies give support to the importance of all three PCC components for learning a good latent space for control.

assistant: text: Learning embedding for control with high-dimensional observations
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: THE LOCAL DIMENSION OF DEEP MANIFOLD

Paper content:
Based on our observation that there exists a dramatic drop for the singular values of the fully connected layers or a single feature map of the convolutional layer, and that the dimension of the concatenated feature vector almost equals the summation of the dimension on each feature map, we propose a singular value decomposition (SVD) based approach to estimate the dimension of the deep manifolds for a typical convolutional neural network VGG19.
We choose three categories from the ImageNet, namely Persian Cat, Container Ship and Volcano, and determine the local dimension of the deep manifolds of the deep layers through the tangent space of a target image.
Through several augmentation methods, we found that the Gaussian noise method is closer to the intrinsic dimension, as by adding random noise to an image we are moving in an arbitrary dimension, and when the rank of the feature matrix of the augmented images does not increase we are very close
to the local dimension of the manifold.
We also estimate the dimension of the deep manifold based on the tangent space for each of the maxpooling layers.
Our results show that the dimensions of different categories are close to each other and decline quickly along the convolutional layers and fully connected layers.
Furthermore, we show that the dimensions decline quickly inside the Conv5 layer.
Our work provides new insights for the intrinsic structure of deep neural networks and helps unveiling the inner organization of the black box of deep neural networks.

assistant: text: We propose a SVD based method to explore the local dimension of activation manifold in deep neural networks.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Cancer homogeneity in single cell revealed by Bi-state model and Binary matrix factorization

Paper content:
Single cell RNA sequencing (scRNAseq) technology enables quantifying gene expression profiles by individual cells within cancer.
Dimension reduction methods have been commonly used for cell clustering analysis and visualization of the data.
Current dimension reduction methods tend overly eliminate the expression variations correspond to less dominating characteristics, such we fail to find the homogenious properties of cancer development.
In this paper, we proposed a new and clustering analysis method for scRNAseq data, namely BBSC, via implementing a binarization of the gene expression profile into on/off frequency changes with a Boolean matrix factorization.
The low rank representation of expression matrix recovered by BBSC increase the resolution in identifying distinct cell types or functions.
Application of BBSC on two cancer scRNAseq data successfully discovered both homogeneous and heterogeneous cancer cell clusters.
Further finding showed potential in preventing cancer progression.

assistant: text: Our finding shed lights in preventing cancer progression
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Few-Shot Learning with Simplex

Paper content:
Deep learning has made remarkable achievement in many fields.
However, learning
the parameters of neural networks usually demands a large amount of labeled
data.
The algorithms of deep learning, therefore, encounter difficulties when applied
to supervised learning where only little data are available.
This specific task
is called few-shot learning.
To address it, we propose a novel algorithm for fewshot
learning using discrete geometry, in the sense that the samples in a class are
modeled as a reduced simplex.
The volume of the simplex is used for the measurement
of class scatter.
During testing, combined with the test sample and the
points in the class, a new simplex is formed.
Then the similarity between the test
sample and the class can be quantized with the ratio of volumes of the new simplex
to the original class simplex.
Moreover, we present an approach to constructing
simplices using local regions of feature maps yielded by convolutional neural networks.
Experiments on Omniglot and miniImageNet verify the effectiveness of
our simplex algorithm on few-shot learning.

assistant: text: A simplex-based geometric method is proposed to cope with few-shot learning problems.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Adversarial Sampling for Active Learning

Paper content:
This paper proposes ASAL, a new pool based active learning method that generates high entropy samples.
Instead of directly annotating the synthetic samples, ASAL searches similar samples from the pool and includes them for training.
Hence, the quality of new samples is high and annotations are reliable.
ASAL is particularly suitable for large data sets because it achieves a better run-time complexity (sub-linear) for sample selection than traditional uncertainty sampling (linear).
We present a comprehensive set of experiments on two data sets and show that ASAL outperforms similar methods and clearly exceeds the established baseline (random sampling).
In the discussion section we analyze in which situations ASAL performs best and why it is sometimes hard to outperform random sample selection.
To the best of our knowledge this is the first adversarial active learning technique that is applied for multiple class problems using deep convolutional classifiers and demonstrates superior performance than random sample selection.

assistant: text: ASAL is a pool based active learning method that generates high entropy samples and retrieves matching samples from the pool in sub-linear time.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Non-Gaussian processes and neural networks at finite widths

Paper content:
Gaussian processes are ubiquitous in nature and engineering.
A case in point is a class of neural networks in the infinite-width limit, whose priors correspond to Gaussian processes.
Here we perturbatively extend this correspondence to finite-width neural networks, yielding non-Gaussian processes as priors.
The methodology developed herein allows us to track the flow of preactivation distributions by progressively integrating out random variables from lower to higher layers, reminiscent of renormalization-group flow.
We further develop a perturbative prescription to perform Bayesian inference with weakly non-Gaussian priors.

assistant: text: We develop an analytical method to study Bayesian inference of finite-width neural networks and find that the renormalization-group flow picture naturally emerges.