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

Title: DDSP: Differentiable Digital Signal Processing

Paper content:
Most generative models of audio directly generate samples in one of two domains: time or frequency.
While sufficient to express any signal, these representations are inefficient, as they do not utilize existing knowledge of how sound is generated and perceived.
A third approach (vocoders/synthesizers) successfully incorporates strong domain knowledge of signal processing and perception, but has been less actively researched due to limited expressivity and difficulty integrating with modern auto-differentiation-based machine learning methods.
In this paper, we introduce the Differentiable Digital Signal Processing (DDSP) library, which enables direct integration of classic signal processing elements with deep learning methods.
Focusing on audio synthesis, we achieve high-fidelity generation without the need for large autoregressive models or adversarial losses, demonstrating that DDSP enables utilizing strong inductive biases without losing the expressive power of neural networks.
Further, we show that combining interpretable modules permits manipulation of each separate model component, with applications such as independent control of pitch and loudness, realistic extrapolation to pitches not seen during training, blind dereverberation of room acoustics, transfer of extracted room acoustics to new environments, and transformation of timbre between disparate sources.
In short, DDSP enables an interpretable and modular approach to generative modeling, without sacrificing the benefits of deep learning.
The library will is available at https://github.com/magenta/ddsp and we encourage further contributions from the community and domain experts.

assistant: text: Better audio synthesis by combining interpretable DSP with end-to-end learning.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Observational Overfitting in Reinforcement Learning

Paper content:
A major component of overfitting in model-free reinforcement learning (RL) involves the case where the agent may mistakenly correlate reward with certain spurious features from the observations generated by the Markov Decision Process (MDP).
We provide a general framework for analyzing this scenario, which we use to design multiple synthetic benchmarks from only modifying the observation space of an MDP.
When an agent overfits to different observation spaces even if the underlying MDP dynamics is fixed, we term this observational overfitting.
Our experiments expose intriguing properties especially with regards to implicit regularization, and also corroborate results from previous works in RL generalization and supervised learning (SL).

assistant: text: We isolate one factor of RL generalization by analyzing the case when the agent only overfits to the observations. We show that architectural implicit regularizations occur in this regime.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: wMAN: WEAKLY-SUPERVISED MOMENT ALIGNMENT NETWORK FOR TEXT-BASED VIDEO SEGMENT RETRIEVAL

Paper content:
Given a video and a sentence, the goal of weakly-supervised video moment retrieval is to locate the video segment which is described by the sentence without having access to temporal annotations during training.
Instead, a model must learn how to identify the correct segment (i.e. moment) when only being provided with video-sentence pairs.
Thus, an inherent challenge is automatically inferring the latent correspondence between visual and language representations.
To facilitate this alignment, we propose our Weakly-supervised Moment Alignment Network (wMAN) which exploits a multi-level co-attention mechanism to learn richer multimodal representations.
The aforementioned mechanism is comprised of a Frame-By-Word interaction module as well as a novel Word-Conditioned Visual Graph (WCVG).
Our approach also incorporates a novel application of positional encodings, commonly used in Transformers, to learn visual-semantic representations that contain contextual information of their relative positions in the temporal sequence through iterative message-passing.
Comprehensive experiments on the DiDeMo and Charades-STA datasets demonstrate the effectiveness of our learned representations: our combined wMAN model not only outperforms the state-of-the-art weakly-supervised method by a significant margin but also does better than strongly-supervised state-of-the-art methods on some metrics.

assistant: text: Weakly-Supervised Text-Based Video Moment Retrieval
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Neural separation of observed and unobserved distributions

Paper content:
Separating mixed distributions is a long standing challenge for machine learning and signal processing.
Applications include: single-channel multi-speaker separation (cocktail party problem), singing voice separation and separating reflections from images.
Most current methods either rely on making strong assumptions on the source distributions (e.g. sparsity, low rank, repetitiveness) or rely on having training samples of each source in the mixture.
In this work, we tackle the scenario of extracting an unobserved distribution additively mixed with a signal from an observed (arbitrary) distribution.
We introduce a new method: Neural Egg Separation - an iterative method that learns to separate the known distribution from progressively finer estimates of the unknown distribution.
In some settings, Neural Egg Separation is initialization sensitive, we therefore introduce GLO Masking which ensures a good initialization.
Extensive experiments show that our method outperforms current methods that use the same level of supervision and often achieves similar performance to full supervision.

assistant: text: An iterative neural method for extracting signals that are only observed mixed with other signals
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Generalization Puzzles in Deep Networks

Paper content:
In the last few years, deep learning has been tremendously successful in many applications.
However, our theoretical understanding of deep learning, and thus the ability of providing principled improvements, seems to lag behind.
A theoretical puzzle concerns the ability of deep networks to predict well despite their intriguing apparent lack of generalization: their classification accuracy on the training set is not a proxy for their performance on a test set.
How is it possible that training performance is independent of testing performance?
Do indeed deep networks require a drastically new theory of generalization?
Or are there measurements based on the training data that are predictive of the network performance on future data?
Here we show that when performance is measured appropriately, the training performance is in fact predictive of expected performance, consistently with classical machine learning theory.

assistant: text: Contrary to previous beliefs, the training performance of deep networks, when measured appropriately, is predictive of test performance, consistent with classical machine learning theory.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: An Investigation of Memory in Recurrent Neural Networks

Paper content:
We investigate the learned dynamical landscape of a recurrent neural network solving a simple task requiring the interaction of two memory mechanisms: long- and short-term.
Our results show that while long-term memory is implemented by asymptotic attractors, sequential recall is now additionally implemented by oscillatory dynamics in a transverse subspace to the basins of attraction of these stable steady states.
Based on our observations, we propose how different types of memory mechanisms can coexist and work together in a single neural network, and discuss possible applications to the fields of artificial intelligence and neuroscience.

assistant: text: We investigate how a recurrent neural network successfully learns a task combining long-term memory and sequential recall.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning Network Parameters in the ReLU Model

Paper content:
Rectified linear units, or ReLUs, have become a preferred activation function for artificial neural networks.
In this paper we consider the problem of learning a generative model in the presence of nonlinearity (modeled by the ReLU functions).
Given a set of signal vectors $\mathbf{y}^i \in \mathbb{R}^d, i =1, 2, \dots , n$, we  aim to learn the network parameters, i.e., the $d\times k$ matrix $A$, under the model $\mathbf{y}^i = \mathrm{ReLU}(A\mathbf{c}^i +\mathbf{b})$, where $\mathbf{b}\in \mathbb{R}^d$ is a random bias vector, and {$\mathbf{c}^i \in \mathbb{R}^k$ are arbitrary unknown latent vectors}.
We show that it is possible to recover the column space of $A$ within an error of $O(d)$ (in Frobenius norm) under certain conditions on the  distribution of $\mathbf{b}$.

assistant: text: We show that it is possible to recover the parameters of a 1-layer ReLU generative model from looking at samples generated by it
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Model-Augmented Actor-Critic: Backpropagating through Paths

Paper content:
Current model-based reinforcement learning approaches use the model simply as a learned black-box simulator to augment the data for policy optimization or value function learning.
In this paper, we show how to make more effective use of the model by exploiting its differentiability.
We construct a policy optimization algorithm that uses the pathwise derivative of the learned model and policy across future timesteps.
Instabilities of learning across many timesteps are prevented by using a terminal value function, learning the policy in an actor-critic fashion.
Furthermore, we present a derivation on the monotonic improvement of our objective in terms of the gradient error in the model and value function.
We show that our approach
(i) is consistently more sample efficient than existing state-of-the-art model-based algorithms,
(ii) matches the asymptotic performance of model-free algorithms, and
(iii) scales to long horizons, a regime where typically past model-based approaches have struggled.

assistant: text: Policy gradient through backpropagation through time using learned models and Q-functions. SOTA results in reinforcement learning benchmark environments.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning Video Representations using Contrastive Bidirectional Transformer

Paper content:
This paper proposes a self-supervised learning approach for video features that results in significantly improved performance on downstream tasks (such as video classification, captioning and segmentation) compared to existing methods.
Our method extends the BERT model for text sequences to the case of sequences of real-valued feature vectors, by replacing the softmax loss with noise contrastive estimation (NCE).
We also show how to learn representations from sequences of visual features and sequences of words derived from ASR (automatic speech recognition), and show that such cross-modal training (when possible) helps even more.

assistant: text: Generalized BERT for continuous and cross-modal inputs; state-of-the-art self-supervised video representations.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Lower Bounds for Compressed Sensing with Generative Models

Paper content:
The goal of compressed sensing is to learn a structured signal $x$
  from a limited number of noisy linear measurements $y
\approx Ax$.  In
  traditional compressed sensing, ``structure'' is represented by
  sparsity in some known basis.
Inspired by the success of deep
  learning in modeling images, recent work starting with~\cite{BDJP17}
  has instead considered structure to come from a generative model
  $G: \R^k \to \R^n$.  We present two results establishing the
  difficulty of this latter task, showing that existing bounds are
  tight.
First, we provide a lower bound matching the~\cite{BDJP17} upper
  bound for compressed sensing from $L$-Lipschitz generative models
  $G$.  In particular, there exists such a function that requires
  roughly $\Omega(k \log L)$ linear measurements for sparse recovery
  to be possible.
This holds even for the more relaxed goal of
  \emph{nonuniform} recovery.
Second, we show that generative models generalize sparsity as a
  representation of structure.
In particular, we construct a
  ReLU-based neural network $G: \R^{2k} \to \R^n$ with $O(1)$ layers
  and $O(kn)$ activations per layer, such that the range of $G$
  contains all $k$-sparse vectors.

assistant: text: Lower bound for compressed sensing w/ generative models that matches known upper bounds
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Width-Based Lookaheads Augmented with Base Policies for Stochastic Shortest Paths

Paper content:
Sequential decision problems for real-world applications often need to be solved in real-time, requiring algorithms to perform well with a restricted computational budget.
Width-based lookaheads have shown state-of-the-art performance in classical planning problems as well as over the Atari games with tight budgets.
In this work we investigate width-based lookaheads over Stochastic Shortest paths (SSP).
We analyse why width-based algorithms perform poorly over SSP problems, and overcome these pitfalls proposing a method to estimate costs-to-go.
We formalize width-based lookaheads as an instance of the rollout algorithm, give a definition of width for SSP problems and explain its sample complexity.
Our experimental results over a variety of SSP benchmarks show the algorithm to outperform other state-of-the-art rollout algorithms such as UCT and RTDP.

assistant: text: We propose a new Monte Carlo Tree Search / rollout algorithm that relies on width-based search to construct a lookahead.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Visualizing and Understanding GANs

Paper content:
We present an analytic framework to visualize and understand GANs at the unit-, object-, and scene-level.
We first identify a group of interpretable units that are closely related to object concepts with a segmentation-based network dissection method.
Then, we examine the causal effect of interpretable units by measuring the ability of interventions to control objects in the output.
Finally, we examine the contextual relationship between these units and their surrounding by inserting the discovered object concepts into new images.
We show several practical applications enabled by our framework, from comparing internal representations across different layers and models, to improving GANs by locating and removing artifact-causing units, to interactively manipulating objects in the scene.

assistant: text: GAN representations are examined in detail, and sets of representation units are found that control the generation of semantic concepts in the output.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: EnGAN: Latent Space MCMC and Maximum Entropy Generators for Energy-based Models

Paper content:
Unsupervised learning is about capturing dependencies between variables and is driven by the contrast between the probable vs improbable configurations of these variables, often either via a generative model which only samples probable ones or with an energy function (unnormalized log-density) which is low for probable ones and high for improbable ones.
Here we consider learning both an energy function and  an efficient approximate sampling mechanism for the corresponding distribution.
Whereas the critic (or discriminator) in generative adversarial networks (GANs) learns to separate data and generator samples, introducing an entropy maximization regularizer on the generator can turn the interpretation of the critic into an energy function, which separates the training distribution from everything else, and thus can be used for tasks like anomaly or novelty detection.
This paper is motivated by the older idea of sampling in latent space rather than data space because running a Monte-Carlo Markov Chain (MCMC) in latent space has been found to be easier and more efficient, and because a GAN-like generator can convert latent space samples to data space samples.
For this purpose, we show how a Markov chain can be run in latent space whose samples can be mapped to data space, producing better samples.
These samples are also used for the negative phase gradient required to estimate the log-likelihood gradient of the data space energy function.
To maximize entropy at the output of the generator, we take advantage of recently introduced neural estimators of mutual information.
We find that in addition to producing a useful scoring function for anomaly detection, the resulting approach produces sharp samples (like GANs) while covering the modes well, leading to high Inception and Fréchet scores.

assistant: text: We introduced entropy maximization to GANs, leading to a reinterpretation of the critic as an energy function.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: DARTS: Differentiable Architecture Search

Paper content:
This paper addresses the scalability challenge of architecture search by formulating the task in a differentiable manner.
Unlike conventional approaches of applying evolution or reinforcement learning over a discrete and non-differentiable search space, our method is based on the continuous relaxation of the architecture representation, allowing efficient search of the architecture using gradient descent.
Extensive experiments on CIFAR-10, ImageNet, Penn Treebank and WikiText-2 show that our algorithm excels in discovering high-performance convolutional architectures for image classification and recurrent architectures for language modeling, while being orders of magnitude faster than state-of-the-art non-differentiable techniques.

assistant: text: We propose a differentiable architecture search algorithm for both convolutional and recurrent networks, achieving competitive performance with the state of the art using orders of magnitude less computation resources.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Bilingual-GAN: Neural Text Generation and Neural Machine Translation as Two Sides of the Same Coin

Paper content:
Latent space based GAN methods and attention based encoder-decoder architectures have achieved impressive results in text generation and Unsupervised NMT respectively.
Leveraging the two domains, we propose an adversarial latent space based architecture capable of generating parallel sentences in two languages concurrently and translating bidirectionally.
The bilingual generation goal is achieved by sampling from the latent space that is adversarially constrained to be shared between both languages.
First an NMT model is trained, with back-translation and an adversarial setup, to enforce a latent state between the two languages.
The encoder and decoder are shared for the two translation directions.
Next, a GAN is trained to generate ‘synthetic’ code mimicking the languages’ shared latent space.
This code is then fed into the decoder to generate text in either language.
We perform our experiments on Europarl and Multi30k datasets, on the English-French language pair, and document our performance using both Supervised and Unsupervised NMT.

assistant: text: We present a novel method for Bilingual Text Generation producing parallel concurrent sentences in two languages.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning Discriminators as Energy Networks in Adversarial Learning

Paper content:
We propose a novel  adversarial learning framework in this work.
Existing adversarial learning methods involve two separate networks, i.e., the structured prediction models and the discriminative models, in the training.
The information captured by discriminative models complements that in the structured prediction models, but few existing researches have studied on utilizing such information to improve structured prediction models at the inference stage.
In this work, we propose to refine the predictions of structured prediction models by effectively integrating discriminative models into the prediction.
Discriminative models are treated as energy-based models.
Similar to the adversarial learning, discriminative models are trained to estimate scores which measure the quality of predicted outputs, while structured prediction models are trained to predict contrastive outputs with maximal energy scores.
In this way, the gradient vanishing problem is ameliorated, and thus we are able to perform inference by following the ascent gradient directions of discriminative models to refine structured prediction models.
The proposed method is able to handle a range of tasks, \emph{e.g.}, multi-label classification and image segmentation.
Empirical results on these two tasks validate the effectiveness of our learning method.

assistant: text: We propose a novel adversarial learning framework for structured prediction, in which discriminative models can be used to refine structured prediction models at the inference stage.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: 

Paper content:
We propose learning to transfer learn (L2TL) to improve transfer learning on a target dataset by judicious extraction of information from a source dataset.
L2TL considers joint optimization of vastly-shared weights between models for source and target tasks, and employs adaptive weights for scaling of constituent losses.
The adaptation of the weights is based on reinforcement learning, guided with a performance metric on the target validation set.
We demonstrate state-of-the-art performance of L2TL given fixed models, consistently outperforming fine-tuning baselines on various datasets.
In the regimes of small-scale target datasets and significant label mismatch between source and target datasets, L2TL outperforms previous work by an even larger margin.

assistant: text: We propose learning to transfer learn (L2TL) to improve transfer learning on a target dataset by judicious extraction of information from a source dataset.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: On Weight-Sharing and Bilevel Optimization in Architecture Search

Paper content:
Weight-sharing—the simultaneous optimization of multiple neural networks using the same parameters—has emerged as a key component of state-of-the-art neural architecture search.
However, its success is poorly understood and often found to be surprising.
We argue that, rather than just being an optimization trick, the weight-sharing approach is induced by the relaxation of a structured hypothesis space, and introduces new algorithmic and theoretical challenges as well as applications beyond neural architecture search.
Algorithmically, we show how the geometry of ERM for weight-sharing requires greater care when designing gradient- based minimization methods and apply tools from non-convex non-Euclidean optimization to give general-purpose algorithms that adapt to the underlying structure.
We further analyze the learning-theoretic behavior of the bilevel optimization solved by practical weight-sharing methods.
Next, using kernel configuration and NLP feature selection as case studies, we demonstrate how weight-sharing applies to the architecture search generalization of NAS and effectively optimizes the resulting bilevel objective.
Finally, we use our optimization analysis to develop a simple exponentiated gradient method for NAS that aligns with the underlying optimization geometry and matches state-of-the-art approaches on CIFAR-10.

assistant: text: An analysis of the learning and optimization structures of architecture search in neural networks and beyond.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Dynamically Learning the Learning Rates:  Online Hyperparameter Optimization

Paper content:
Hyperparameter tuning is arguably the most important ingredient for obtaining state of art performance in deep networks.
We focus on hyperparameters  that are related to the optimization algorithm, e.g.  learning rates, which have a large impact on the training speed and the resulting accuracy.
Typically, fixed learning rate schedules are employed during training.
We propose Hyperdyn a dynamic hyperparameter optimization method that selects new learning rates on the fly at the end of each epoch.
Our explore-exploit   framework  combines Bayesian optimization (BO) with a rejection strategy, based on a simple probabilistic wait and watch test.
We obtain state of  art accuracy results on CIFAR and Imagenet datasets, but with significantly faster training, when compared with the best manually tuned networks.

assistant: text: Bayesian optimization based online hyperparameter optimization.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Order Learning and Its Application to Age Estimation

Paper content:
We propose order learning to determine the order graph of classes, representing ranks or priorities, and classify an object instance into one of the classes.
To this end, we design a pairwise comparator to categorize the relationship between two instances into one of three cases: one instance is `greater than,' `similar to,' or `smaller than' the other.
Then, by comparing an input instance with reference instances and maximizing the consistency among the comparison results, the class of the input can be estimated reliably.
We apply order learning to develop a facial age estimator, which provides the state-of-the-art performance.
Moreover, the performance is further improved when the order graph is divided into disjoint chains using gender and ethnic group information or even in an unsupervised manner.

assistant: text: The notion of order learning is proposed and it is applied to regression problems in computer vision
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Jumpout: Improved Dropout for Deep Neural Networks with Rectified Linear Units

Paper content:
Dropout is a simple yet effective technique to improve generalization performance and prevent overfitting in deep neural networks (DNNs).
In this paper, we discuss three novel observations about dropout to better understand the generalization of DNNs with rectified linear unit (ReLU) activations: 1) dropout is a smoothing technique that encourages each local linear model of a DNN to be trained on data points from nearby regions; 2) a constant dropout rate can result in effective neural-deactivation rates that are significantly different for layers with different fractions of activated neurons; and 3) the rescaling factor of dropout causes an inconsistency to occur between the normalization during training and testing conditions when batch normalization is also used.
The above leads to three simple but nontrivial improvements to dropout resulting in our proposed method "Jumpout.
"
Jumpout samples the dropout rate using a monotone decreasing distribution (such as the right part of a truncated Gaussian), so the local linear model at each data point is trained, with high probability, to work better for data points from nearby than from more distant regions.
Instead of tuning a dropout rate for each layer and applying it to all samples, jumpout moreover adaptively normalizes the dropout rate at each layer and every training sample/batch, so the effective dropout rate applied to the activated neurons are kept the same.
Moreover, we rescale the outputs of jumpout for a better trade-off that keeps both the variance and mean of neurons more consistent between training and test phases, which mitigates the incompatibility between dropout and batch normalization.
Compared to the original dropout, jumpout shows significantly improved performance on CIFAR10, CIFAR100, Fashion- MNIST, STL10, SVHN, ImageNet-1k, etc., while introducing negligible additional memory and computation costs.

assistant: text: Jumpout applies three simple yet effective modifications to dropout, based on novel understandings about the generalization performance of DNN with ReLU in local regions.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Semi-supervised classification by reaching consensus among modalities

Paper content:
We extend the Consensus Network framework to Transductive Consensus Network (TCN), a semi-supervised multi-modal classification framework, and identify its two mechanisms: consensus and classification.
By putting forward three variants as ablation studies, we show both mechanisms should be functioning together.
Overall, TCNs outperform or align with the best benchmark algorithms when only 20 to 200 labeled data points are available.

assistant: text: A semi-supervised multi-modal classification framework, TCN, that outperforms various benchmarks.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Graph-based motion planning networks

Paper content:
Differentiable planning network architecture has shown to be powerful in solving transfer planning tasks while possesses a simple end-to-end training feature.
Many great planning architectures that have been proposed later in literature are inspired by this design principle in which a recursive network architecture is applied to emulate backup operations of a value  iteration algorithm.
However existing frame-works can only learn and plan effectively on domains with a lattice structure, i.e. regular graphs embedded in a certain Euclidean space.
In this paper, we propose a general planning network, called Graph-based Motion Planning Networks (GrMPN), that will be able to
i) learn and plan on general irregular graphs, hence
ii) render existing planning network architectures special cases.
The proposed GrMPN framework is invariant to task graph permutation, i.e. graph isormophism.
As a result, GrMPN possesses the generalization strength and data-efficiency ability.
We demonstrate the performance of the proposed GrMPN method against other baselines on three domains ranging from 2D mazes (regular graph), path planning on irregular graphs, and motion planning (an irregular graph of robot configurations).

assistant: text: We propose an end-to-end differentiable planning network for graphs. This can be applicable to many motion planning problems
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: EnergyNet: Energy-Efficient Dynamic Inference

Paper content:
The prohibitive energy cost of running high-performance Convolutional Neural Networks (CNNs) has been limiting their deployment on resource-constrained platforms including mobile and wearable devices.
We propose a CNN for energy-aware dynamic routing, called the EnergyNet, that achieves adaptive-complexity inference based on the inputs, leading to an overall reduction of run time energy cost without noticeably losing (or even improving) accuracy.
That is achieved by proposing an energy loss that captures both computational and data movement costs.
We combine it with the accuracy-oriented loss, and learn a dynamic routing policy for skipping certain layers in the networks, that optimizes the hybrid loss.
Our empirical results demonstrate that, compared to the baseline CNNs, EnergyNetcan trim down the energy cost up to 40% and 65%, during inference on the CIFAR10 and Tiny ImageNet testing sets, respectively, while maintaining the same testing accuracies.
It is further encouraging to observe that the energy awareness might serve as a training regularization and can even improve prediction accuracy:  our models can achieve 0.7% higher top-1 testing accuracy than the baseline on CIFAR-10 when saving up to 27% energy, and 1.0% higher top-5 testing accuracy on Tiny ImageNet when saving up to 50% energy, respectively.

assistant: text: This paper proposes a new CNN model that combines energy cost with a dynamic routing strategy to enable adaptive energy-efficient inference.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Cross-View Training for Semi-Supervised Learning

Paper content:
We present Cross-View Training (CVT), a simple but effective method for deep semi-supervised learning.
On labeled examples, the model is trained with standard cross-entropy loss.
On an unlabeled example, the model first performs inference (acting as a "teacher") to produce soft targets.
The model then learns from these soft targets (acting as a ``"student").
We deviate from prior work by adding multiple auxiliary student prediction layers to the model.
The input to each student layer is a sub-network of the full model that has a restricted view of the input  (e.g., only seeing one region of an image).
The students can learn from the teacher (the full model) because the teacher sees more of each example.
Concurrently, the students improve the quality of the representations used by the teacher as they learn to make predictions with limited data.
When combined with Virtual Adversarial Training, CVT improves upon the current state-of-the-art on semi-supervised CIFAR-10 and semi-supervised SVHN.
We also apply CVT to train models on five natural language processing tasks using hundreds of millions of sentences of unlabeled data.
On all tasks CVT substantially outperforms supervised learning alone, resulting in models that improve upon or are competitive with the current state-of-the-art.

assistant: text: Self-training with different views of the input gives excellent results for semi-supervised image recognition, sequence tagging, and dependency parsing.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: STCN: Stochastic Temporal Convolutional Networks

Paper content:
Convolutional architectures have recently been shown to be competitive on many
sequence modelling tasks when compared to the de-facto standard of recurrent neural networks (RNNs) while providing computational and modelling advantages due to inherent parallelism.
However, currently, there remains a performance
gap to more expressive stochastic RNN variants, especially those with several layers of dependent random variables.
In this work, we propose stochastic temporal convolutional networks (STCNs), a novel architecture that combines the computational advantages of temporal convolutional networks (TCN) with the representational power and robustness of stochastic latent spaces.
In particular, we propose a hierarchy of stochastic latent variables that captures temporal dependencies at different time-scales.
The architecture is modular and flexible due to the decoupling of the deterministic and stochastic layers.
We show that the proposed architecture achieves state of the art log-likelihoods across several tasks.
Finally, the model is capable of predicting high-quality synthetic samples over a long-range temporal horizon in modelling of handwritten text.

assistant: text: We combine the computational advantages of temporal convolutional architectures with the expressiveness of stochastic latent variables.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Weakly Supervised Clustering by Exploiting Unique Class Count

Paper content:
A weakly supervised learning based clustering framework is proposed in this paper.
As the core of this framework, we introduce a novel multiple instance learning task based on a bag level label called unique class count (ucc), which is the number of unique classes among all instances inside the bag.
In this task, no annotations on individual instances inside the bag are needed during training of the models.
We mathematically prove that with a perfect ucc classifier, perfect clustering of individual instances inside the bags is possible even when no annotations on individual instances are given during training.
We have constructed a neural network based ucc classifier and experimentally shown that the clustering performance of our framework with our weakly supervised ucc classifier is comparable to that of fully supervised learning models where labels for all instances are known.
Furthermore, we have tested the applicability of our framework to a real world task of semantic segmentation of breast cancer metastases in histological lymph node sections and shown that the performance of our weakly supervised framework is comparable to the performance of a fully supervised Unet model.

assistant: text: A weakly supervised learning based clustering framework performs comparable to that of fully supervised learning models by exploiting unique class count.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: INTERNAL-CONSISTENCY CONSTRAINTS FOR EMERGENT COMMUNICATION

Paper content:
When communicating, humans rely on internally-consistent language representations.
That is, as speakers, we expect listeners to behave the same way we do when we listen.
This work proposes several methods for encouraging such internal consistency in dialog agents in an emergent communication setting.
We consider two hypotheses about the effect of internal-consistency constraints:
1) that they improve agents’ ability to refer to unseen referents, and
2) that they improve agents’ ability to generalize across communicative roles (e.g. performing as a speaker de- spite only being trained as a listener).
While we do not find evidence in favor of the former, our results show significant support for the latter.

assistant: text: Internal-consistency constraints improve agents ability to develop emergent protocols that generalize across communicative roles.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Generative Code Modeling with Graphs

Paper content:
Generative models forsource code are an interesting structured prediction problem, requiring to reason about both hard syntactic and semantic constraints as well as about natural, likely programs.
We present a novel model for this problem that uses a graph to represent the intermediate state of the generated output.
Our model generates code by interleaving grammar-driven expansion steps with graph augmentation and neural message passing steps.
An experimental evaluation shows that our new model can generate semantically meaningful expressions, outperforming a range of strong baselines.

assistant: text: Representing programs as graphs including semantics helps when generating programs
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Reinforced Genetic Algorithm Learning for Optimizing Computation Graphs

Paper content:
We present a deep reinforcement learning approach to minimizing the execution cost of neural network computation graphs in an optimizing compiler.
Unlike earlier learning-based works that require training the optimizer on the same graph to be optimized, we propose a learning approach that trains an optimizer offline and then generalizes to previously unseen graphs without further training.
This allows our approach to produce high-quality execution decisions on real-world TensorFlow graphs in seconds instead of hours.
We consider two optimization tasks for computation graphs: minimizing running time and peak memory usage.
In comparison to an extensive set of baselines, our approach achieves significant improvements over classical and other learning-based methods on these two tasks.

assistant: text: We use deep RL to learn a policy that directs the search of a genetic algorithm to better optimize the execution cost of computation graphs, and show improved results on real-world TensorFlow graphs.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Maximally Consistent Sampling and the Jaccard Index of Probability Distributions

Paper content:
We introduce simple, efficient algorithms for computing a MinHash of a probability distribution, suitable for both sparse and dense data, with equivalent running times to the state of the art for both cases.
The collision probability of these algorithms is a new measure of the similarity of positive vectors which we investigate in detail.
We describe the sense in which this collision probability is optimal for any Locality Sensitive Hash based on sampling.
We argue that this similarity measure is more useful for probability distributions than the similarity pursued by other algorithms for weighted MinHash, and is the natural generalization of the Jaccard index.

assistant: text: The minimum of a set of exponentially distributed hashes has a very useful collision probability that generalizes the Jaccard Index to probability distributions.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: TD Learning with Constrained Gradients

Paper content:
Temporal Difference Learning with function approximation is known to be unstable.
Previous work like \citet{sutton2009fast} and \citet{sutton2009convergent} has presented alternative objectives that are stable to minimize.
However, in practice, TD-learning with neural networks requires various tricks like using a target network that updates slowly \citep{mnih2015human}.
In this work we propose a constraint on the TD update that minimizes change to the target values.
This constraint can be applied to the gradients of any TD objective, and can be easily applied to nonlinear function approximation.
We validate this update by applying our technique to deep Q-learning, and training without a target network.
We also show that adding this constraint on Baird's counterexample keeps Q-learning from diverging.

assistant: text: We show that adding a constraint to TD updates stabilizes learning and allows Deep Q-learning without a target network
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: How to measure the consistency of the tagging of scientific papers?

Paper content:
A collection of scientific papers is often accompanied by tags:
  keywords, topics, concepts etc., associated with each paper.
Sometimes these tags are human-generated, sometimes they are
  machine-generated.
We propose a simple measure of the consistency
  of the tagging of scientific papers: whether these tags are
  predictive for the citation graph links.
Since the authors tend to
  cite papers about the topics close to those of their publications, a
  consistent tagging system could predict citations.
We present an
  algorithm to calculate consistency, and experiments with human- and
  machine-generated tags.
We show that augmentation, i.e. the combination
  of the manual tags with the machine-generated ones, can enhance the
  consistency of the tags.
We further introduce cross-consistency,
  the ability to predict citation links between papers tagged by
  different taggers, e.g. manually and by a machine.
Cross-consistency can be used to evaluate the tagging quality when
  the amount of labeled data is limited.

assistant: text: A good tagger gives similar tags to a given paper and the papers it cites
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: From Language to Goals: Inverse Reinforcement Learning for Vision-Based Instruction Following

Paper content:
Reinforcement learning is a promising framework for solving control problems, but its use in practical situations is hampered by the fact that reward functions are often difficult to engineer.
Specifying goals and tasks for autonomous machines, such as robots, is a significant challenge: conventionally, reward functions and goal states have been used to communicate objectives.
But people can communicate objectives to each other simply by describing or demonstrating them.
How can we build learning algorithms that will allow us to tell machines what we want them to do?
In this work, we investigate the problem of grounding language commands as reward functions using inverse reinforcement learning, and argue that language-conditioned rewards are more transferable than language-conditioned policies to new environments.
We propose language-conditioned reward learning (LC-RL), which grounds language commands as a reward function represented by a deep neural network.
We demonstrate that our model learns rewards that transfer to novel tasks and environments on realistic, high-dimensional visual environments with natural language commands, whereas directly learning a language-conditioned policy leads to poor performance.

assistant: text: We ground language commands in a high-dimensional visual environment by learning language-conditioned rewards using inverse reinforcement learning.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning Noise-Invariant Representations for Robust Speech Recognition

Paper content:
Despite rapid advances in speech recognition, current models remain brittle to superficial perturbations to their inputs.
Small amounts of noise can destroy the performance of an otherwise state-of-the-art model.
To harden models against background noise, practitioners often perform data augmentation, adding artificially-noised examples to the training set, carrying over the original label.
In this paper, we hypothesize that a clean example and its superficially perturbed counterparts shouldn't merely map to the same class--- they should map to the same representation.
We propose invariant-representation-learning (IRL): At each training iteration, for each training example, we sample a noisy counterpart.
We then apply a penalty term to coerce matched representations at each layer (above some chosen layer).
Our key results, demonstrated on the LibriSpeech dataset are the following:
(i) IRL significantly reduces character error rates (CER)on both `clean' (3.3% vs 6.5%) and `other' (11.0% vs 18.1%) test sets;
(ii) on several out-of-domain noise settings (different from those seen during training), IRL's benefits are even more pronounced.
Careful ablations confirm that our results are not simply due to shrinking activations at the chosen layers.

assistant: text: In this paper, we hypothesize that superficially perturbed data points shouldn’t merely map to the same class---they should map to the same representation.