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

Title: Beyond GANs: Transforming without a Target Distribution

Paper content:
While generative neural networks can learn to transform a specific input dataset into a specific target dataset, they require having just such a paired set of input/output datasets.
For instance, to fool the discriminator, a generative adversarial network (GAN) exclusively trained to transform images of black-haired *men* to blond-haired *men* would need to change gender-related characteristics as well as hair color when given images of black-haired *women* as input.
This is problematic, as often it is possible to obtain *a* pair of (source, target) distributions but then have a second source distribution where the target distribution is unknown.
The computational challenge is that generative models are good at generation within the manifold of the data that they are trained on.
However, generating new samples outside of the manifold or extrapolating "out-of-sample" is a much harder problem that has been less well studied.
To address this, we introduce a technique called *neuron editing* that learns how neurons encode an edit for a particular transformation in a latent space.
We use an autoencoder to decompose the variation within the dataset into activations of different neurons and generate transformed data by defining an editing transformation on those neurons.
By performing the transformation in a latent trained space, we encode fairly complex and non-linear transformations to the data with much simpler distribution shifts to the neuron's activations.
Our technique is general and works on a wide variety of data domains and applications.
We first demonstrate it on image transformations and then move to our two main biological applications: removal of batch artifacts representing unwanted noise and modeling the effect of drug treatments to predict synergy between drugs.

assistant: text: A method for learning a transformation between one pair of source/target datasets and applying it a separate source dataset for which there is no target dataset
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: SNODE: Spectral Discretization of Neural ODEs for System Identification

Paper content:
This paper proposes the use of spectral element methods \citep{canuto_spectral_1988} for fast and accurate training of Neural Ordinary Differential Equations (ODE-Nets; \citealp{Chen2018NeuralOD}) for system identification.
This is achieved by expressing their dynamics as a truncated series of Legendre polynomials.
The series coefficients, as well as the network weights, are computed by minimizing the weighted sum of the loss function and the violation of the ODE-Net dynamics.
The problem is solved by coordinate descent that alternately minimizes, with respect to the coefficients and the weights, two unconstrained sub-problems using standard backpropagation and gradient methods.
The resulting optimization scheme is fully time-parallel and results in a low memory footprint.
Experimental comparison to standard methods, such as backpropagation through explicit solvers and the adjoint technique \citep{Chen2018NeuralOD}, on training surrogate models of small and medium-scale dynamical systems shows that it is at least one order of magnitude faster at reaching a comparable value of the loss function.
The corresponding testing MSE is one order of magnitude smaller as well, suggesting generalization capabilities increase.
Neural Ordinary Differential Equations (ODE-Nets; Chen et al., 2018) can learn latent models from observations that are sparse in time.
This property has the potential to enhance the performance of neural network predictive models in applications where information is sparse in time and it is important to account for exact arrival times and delays.
In complex control systems and model-based reinforcement learning, planning over a long horizon is often needed, while high frequency feedback is necessary for maintaining stability (Franklin et al., 2014) .
Discrete-time models, including RNNs (Jain & Medsker, 1999) , often struggle to fully meet the needs of such applications due to the fixed time resolution.
ODE-Nets have been shown to provide superior performance with respect to classic RNNs on time series forecasting with sparse training data.
However, learning their parameters can be computationally intensive.
In particular, ODE-Nets are memory efficient but time inefficient.
In this paper, we address this bottleneck and propose a novel alternative strategy for system identification.

assistant: text: This paper proposes the use of spectral element methods for fast and accurate training of Neural Ordinary Differential Equations for system identification.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Deep Learning of Determinantal Point Processes via Proper Spectral Sub-gradient

Paper content:
Determinantal point processes (DPPs) is an effective tool to deliver diversity on multiple machine learning and computer vision tasks.
Under deep learning framework, DPP is typically optimized via approximation, which is not straightforward and has some conflict with diversity requirement.
We note, however, there has been no deep learning paradigms to optimize DPP directly since it involves matrix inversion which may result in highly computational instability.
This fact greatly hinders the wide use of DPP on some specific objectives where DPP serves as a term to measure the feature diversity.
In this paper, we devise a simple but effective algorithm to address this issue to optimize DPP term directly expressed with L-ensemble in spectral domain over gram matrix, which is more flexible than learning on parametric kernels.
By further taking into account some geometric constraints, our algorithm seeks to generate valid sub-gradients of DPP term in case when the DPP gram matrix is not invertible (no gradients exist in this case).
In this sense, our algorithm can be easily incorporated with multiple deep learning tasks.
Experiments show the effectiveness of our algorithm, indicating promising performance for practical learning problems.

assistant: text: We proposed a specific back-propagation method via proper spectral sub-gradient to integrate determinantal point process to deep learning framework.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Nesterov's method is the discretization of a differential equation with Hessian damping

Paper content:
Su-Boyd-Candes (2014) made a connection between Nesterov's method and an ordinary differential equation (ODE).
We show if a Hessian damping term is added to the ODE from Su-Boyd-Candes (2014), then Nesterov's method arises as a straightforward discretization of the modified ODE.
Analogously,  in the strongly convex case, a Hessian damping term is added to Polyak's ODE, which is then discretized to yield Nesterov's method for strongly convex functions.
Despite the Hessian term, both second order ODEs can be represented as first order systems.
Established Liapunov analysis is used to recover the accelerated rates of convergence in both continuous and discrete time.
Moreover, the Liapunov analysis can be extended to the case of stochastic gradients which allows the full gradient case to be considered as a special case of the stochastic case.
The result is a unified approach to convex acceleration in both continuous and discrete time and in  both the stochastic and full gradient cases.

assistant: text: We derive Nesterov's method arises as a straightforward discretization of an ODE different from the one in Su-Boyd-Candes and prove acceleration the stochastic case
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: The Comparative Power of ReLU Networks and Polynomial Kernels in the Presence of Sparse Latent Structure

Paper content:
There has been a large amount of interest, both in the past and particularly recently, into the relative advantage of different families of universal function approximators, for instance neural networks, polynomials, rational functions, etc.
However, current research has focused almost exclusively on understanding this problem in a worst case setting: e.g. characterizing the best L1 or L_{infty} approximation in a box (or sometimes, even under an adversarially constructed data distribution.)
In this setting many classical tools from approximation theory can be effectively used.
However, in typical applications we expect data to be high dimensional, but structured -- so, it would only be important to approximate the desired function well on the relevant part of its domain, e.g. a small manifold on which real input data actually lies.
Moreover, even within this domain the desired quality of approximation may not be uniform; for instance in classification problems, the approximation needs to be more accurate near the decision boundary.
These issues, to the best of our knowledge, have remain unexplored until now.
With this in mind, we analyze the performance of neural networks and polynomial kernels in a natural regression setting where the data enjoys sparse latent structure, and the labels depend in a simple way on the latent variables.
We give an almost-tight theoretical analysis of the performance of both neural networks and polynomials for this problem, as well as verify our theory with simulations.
Our results both involve new (complex-analytic) techniques, which may be of independent interest, and show substantial qualitative differences with what is known in the worst-case setting.

assistant: text: Beyond-worst-case analysis of the representational power of  ReLU nets & polynomial kernels  -- in particular in the presence of sparse latent structure.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Out-of-Sample Extrapolation with Neuron Editing

Paper content:
While neural networks can be trained to map from one specific dataset to another, they usually do not learn a generalized transformation that can extrapolate accurately outside the space of training.
For instance, a generative adversarial network (GAN) exclusively trained to transform images of cars from light to dark might not have the same effect on images of horses.
This is because neural networks are good at generation within the manifold of the data that they are trained on.
However, generating new samples outside of the manifold or extrapolating "out-of-sample" is a much harder problem that has been less well studied.
To address this, we introduce a technique called neuron editing that learns how neurons encode an edit for a particular transformation in a latent space.
We use an autoencoder to decompose the variation within the dataset into activations of different neurons and generate transformed data by defining an editing transformation on those neurons.
By performing the transformation in a latent trained space, we encode fairly complex and non-linear transformations to the data with much simpler distribution shifts to the neuron's activations.
We showcase our technique on image domain/style transfer and two biological applications: removal of batch artifacts representing unwanted noise and modeling the effect of drug treatments to predict synergy between drugs.

assistant: text: We reframe the generation problem as one of editing existing points, and as a result extrapolate better than traditional GANs.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Can gradient clipping mitigate label noise?

Paper content:
Gradient clipping is a widely-used technique in the training of deep networks, and is generally motivated from an optimisation lens: informally, it controls the dynamics of iterates, thus enhancing the rate of convergence to a local minimum.
This intuition has been made precise in a line of recent works, which show that suitable clipping  can yield significantly faster convergence than vanilla gradient descent.
In this paper, we propose a new lens for studying gradient clipping, namely, robustness: informally, one expects clipping to provide robustness to noise, since one does not overly trust any single sample.
Surprisingly, we prove that  for the common problem of label noise in classification, standard gradient clipping does not in general provide robustness.
On the other hand, we show that  a simple variant of gradient clipping is provably robust, and corresponds to suitably modifying the underlying loss function.
This yields a simple, noise-robust alternative to the standard cross-entropy loss which performs well empirically.

assistant: text: Gradient clipping doesn't endow robustness to label noise, but a simple loss-based variant does.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Towards Neural Similarity Evaluator

Paper content:
We review three limitations of BLEU and ROUGE – the most popular metrics
used to assess reference summaries against hypothesis summaries, come up with
criteria for what a good metric should behave like and propose concrete ways to
assess the performance of a metric in detail and show the potential of Transformers-based Language Models to assess reference summaries against hypothesis summaries.

assistant: text: New method for assessing the quaility of similarity evaluators and showing potential of Transformer-based language models in replacing BLEU and ROUGE.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning Self-Imitating Diverse Policies

Paper content:
The success of popular algorithms for deep reinforcement learning, such as policy-gradients and Q-learning, relies heavily on the availability of an informative reward signal at each timestep of the sequential decision-making process.
When rewards are only sparsely available during an episode, or a rewarding feedback is provided only after episode termination, these algorithms perform sub-optimally due to the difficultly in credit assignment.
Alternatively, trajectory-based policy optimization methods, such as cross-entropy method and evolution strategies, do not require per-timestep rewards, but have been found to suffer from high sample complexity by completing forgoing the temporal nature of the problem.
Improving the efficiency of RL algorithms in real-world problems with sparse or episodic rewards is therefore a pressing need.
In this work, we introduce a self-imitation learning algorithm that exploits and explores well in the sparse and episodic reward settings.
We view each policy as a state-action visitation distribution and formulate policy optimization as a divergence minimization problem.
We show that with Jensen-Shannon divergence, this divergence minimization problem can be reduced into a policy-gradient algorithm with shaped rewards learned from experience replays.
Experimental results indicate that our algorithm works comparable to existing algorithms in environments with dense rewards, and significantly better in environments with sparse and episodic rewards.
We then discuss limitations of self-imitation learning, and propose to solve them by using Stein variational policy gradient descent with the Jensen-Shannon kernel to learn multiple diverse policies.
We demonstrate its effectiveness on a challenging variant of continuous-control MuJoCo locomotion tasks.

assistant: text: Policy optimization by using past good rollouts from the agent; learning shaped rewards via divergence minimization; SVPG with JS-kernel for population-based exploration.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Invariant Feature Learning by Attribute Perception Matching

Paper content:
An adversarial feature learning (AFL) is a powerful framework to learn representations invariant to a nuisance attribute, which uses an adversarial game between a feature extractor and a categorical attribute classifier.
It theoretically sounds in term of it maximize conditional entropy between attribute and representation.
However, as shown in this paper, the AFL often causes unstable behavior that slows down the convergence.
We propose an {\em attribute perception matching} as an alternative approach, based on the reformulation of conditional entropy maximization as {\em pair-wise distribution matching}. Although the naive approach for realizing the pair-wise distribution matching requires the significantly large number of parameters, the proposed method requires the same number of parameters with AFL but has a better convergence property.
Experiments on both toy and real-world dataset prove that our proposed method converges to better invariant representation significantly faster than AFL.

assistant: text: This paper proposes a new approach to incorporating desired invariance to representations learning, based on the observations that the current state-of-the-art AFL has practical issues.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Neural Machine Translation with Universal Visual Representation

Paper content:
Though visual information has been introduced for enhancing neural machine translation (NMT), its effectiveness strongly relies on the availability of large amounts of bilingual parallel sentence pairs with manual image annotations.
In this paper, we present a universal visual representation learned over the monolingual corpora with image annotations, which overcomes the lack of large-scale bilingual sentence-image pairs, thereby extending image applicability in NMT.
In detail, a group of images with similar topics to the source sentence will be retrieved from a light topic-image lookup table learned over the existing sentence-image pairs, and then is encoded as image representations by a pre-trained ResNet.
An attention layer with a gated weighting is to fuse the visual information and text information as input to the decoder for predicting target translations.
In particular, the proposed method enables the visual information to be integrated into large-scale text-only NMT in addition to the multimodel NMT.
Experiments on four widely used translation datasets, including the WMT'16 English-to-Romanian, WMT'14 English-to-German, WMT'14 English-to-French, and Multi30K, show that the proposed approach achieves significant improvements over strong baselines.

assistant: text: This work proposed a universal visual representation for neural machine translation (NMT) using retrieved images with similar topics to source sentence,  extending image applicability in NMT.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning To Simulate

Paper content:
Simulation is a useful tool in situations where training data for machine learning models is costly to annotate or even hard to acquire.
In this work, we propose a reinforcement learning-based method for automatically adjusting the parameters of any (non-differentiable) simulator, thereby controlling the distribution of synthesized data in order to maximize the accuracy of a model trained on that data.
In contrast to prior art that hand-crafts these simulation parameters or adjusts only parts of the available parameters, our approach fully controls the simulator with the actual underlying goal of maximizing accuracy, rather than mimicking the real data distribution or randomly generating a large volume of data.
We find that our approach
(i) quickly converges to the optimal simulation parameters in controlled experiments and
(ii) can indeed discover good sets of parameters for an image rendering simulator in actual computer vision applications.

assistant: text: We propose an algorithm that automatically adjusts parameters of a simulation engine to generate training data for a neural network such that validation accuracy is maximized.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Revisit Recurrent Attention Model from an Active Sampling Perspective

Paper content:
We revisit the Recurrent Attention Model (RAM, Mnih et al. (2014)), a recurrent neural network for visual attention, from an active information sampling perspective.
We borrow ideas from neuroscience research on the role of active information sampling in the context of visual attention and gaze (Gottlieb, 2018), where the author suggested three types of motives for active information sampling strategies.
We find the original RAM model only implements one of them.
We identify three key weakness of the original RAM and provide a simple solution by adding two extra terms on the objective function.
The modified RAM
1) achieves faster convergence,
2) allows dynamic decision making per sample without loss of accuracy, and
3) generalizes much better on longer sequence of glimpses which is not trained for, compared with the original RAM.

assistant: text: Inspired by neuroscience research, solve three key weakness of the widely-cited recurrent attention model by simply adding two terms on the objective function.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Improving Gaussian mixture latent variable model convergence with Optimal Transport

Paper content:
Generative models with both discrete and continuous latent variables are highly motivated by the structure of many real-world data sets.
They present, however, subtleties in training often manifesting in the discrete latent variable not being leveraged.
In this paper, we show why such models struggle to train using traditional log-likelihood maximization, and that they are amenable to training using the Optimal Transport framework of Wasserstein Autoencoders.
We find our discrete latent variable to be fully leveraged by the model when trained, without any modifications to the objective function or significant fine tuning.
Our model generates comparable samples to other approaches while using relatively simple neural networks, since the discrete latent variable carries much of the descriptive burden.
Furthermore, the discrete latent provides significant control over generation.

assistant: text: This paper shows that the Wasserstein distance objective enables the training of latent variable models with discrete latents in a case where the Variational Autoencoder objective fails to do so.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: 

Paper content:
We present trellis networks, a new architecture for sequence modeling.
On the one hand, a trellis network is a temporal convolutional network with special structure, characterized by weight tying across depth and direct injection of the input into deep layers.
On the other hand, we show that truncated recurrent networks are equivalent to trellis networks with special sparsity structure in their weight matrices.
Thus trellis networks with general weight matrices generalize truncated recurrent networks.
We leverage these connections to design high-performing trellis networks that absorb structural and algorithmic elements from both recurrent and convolutional models.
Experiments demonstrate that trellis networks outperform the current state of the art methods on a variety of challenging benchmarks, including word-level language modeling and character-level language modeling tasks, and stress tests designed to evaluate long-term memory retention.
The code is available at https://github.com/locuslab/trellisnet .

assistant: text: Trellis networks are a new sequence modeling architecture that bridges recurrent and convolutional models and sets a new state of the art on word- and character-level language modeling.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Efficient Dictionary Learning with Gradient Descent

Paper content:
Randomly initialized first-order optimization algorithms are the method of choice for solving many high-dimensional nonconvex problems in machine learning, yet general theoretical guarantees cannot rule out convergence to critical points of poor objective value.
For some highly structured nonconvex problems however, the success of gradient descent can be understood by studying the geometry of the objective.
We study one such problem -- complete orthogonal dictionary learning, and provide converge guarantees for randomly initialized gradient descent to the neighborhood of a global optimum.
The resulting rates scale as low order polynomials in the dimension even though the objective possesses an exponential number of saddle points.
This efficient convergence can be viewed as a consequence of negative curvature normal to the stable manifolds associated with saddle points, and we provide evidence that this feature is shared by other nonconvex problems of importance as well.

assistant: text: We provide an efficient convergence rate for gradient descent on the complete orthogonal dictionary learning objective based on a geometric analysis.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Meta-Reinforcement Learning for Adaptive Autonomous Driving

Paper content:
Reinforcement learning (RL) methods achieved major advances in multiple tasks surpassing human performance.
However, most of RL strategies show a certain degree of weakness and may become computationally intractable when dealing with high-dimensional and non-stationary environments.
In this paper, we build a meta-reinforcement learning (MRL) method embedding an adaptive neural network (NN) controller for efficient policy iteration in changing task conditions.
Our main goal is to extend RL application to the challenging task of urban autonomous driving in CARLA simulator.

assistant: text: A meta-reinforcement learning approach embedding a neural network controller applied to autonomous driving with Carla simulator.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Predictive Coding, Variational Autoencoders, and Biological Connections

Paper content:
Predictive coding, within theoretical neuroscience, and variational autoencoders, within machine learning, both involve latent Gaussian models and variational inference.
While these areas share a common origin, they have evolved largely independently.
We outline connections and contrasts between these areas, using their relationships to identify new parallels between machine learning and neuroscience.
We then discuss specific frontiers at this intersection: backpropagation, normalizing flows, and attention, with mutual benefits for both fields.
Perception has been conventionally formulated as hierarchical feature detection [52] , similar to discriminative deep networks [34] .
In contrast, predictive coding [48, 14] and variational autoencoders (VAEs) [31, 51] frame perception as a generative process, modeling data observations to learn and infer aspects of the external environment.
Specifically, both areas model observations, x, using latent variables, z, through a probabilistic model, p θ (x, z) = p θ (x|z)p θ (z).
Both areas also use variational inference, introducing an approximate posterior, q(z|x), to infer z and learn the model parameters, θ.
These similarities are the result of a common origin, with Mumford [45] , Dayan et al. [9] , and others [46] formalizing earlier ideas [59, 38] .
However, since their inception, these areas have developed largely independently.
We explore their relationships (see also [58, 37] ) and highlight opportunities for the transfer of ideas.
In identifying these ties, we hope to strengthen this promising, close connection between neuroscience and machine learning, prompting further investigation.
We have identified commonalities between predictive coding and VAEs, discussing new frontiers resulting from this perspective.
Reuniting these areas may strengthen the connection between neuroscience and machine learning.
Further refining this connection could lead to mutual benefits: neuroscience can offer inspiration for investigation in machine learning, and machine learning can evaluate ideas on real-world datasets and environments.
Indeed, despite some push back [17] , if predictive coding and related theories [18] are to become validated descriptions of the brain and overcome their apparent generality, they will likely require the computational tools and ideas of modern machine learning to pin down and empirically compare design choices.

assistant: text: connections between predictive coding and VAEs + new frontiers
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Fine-grained Entity Recognition with Reduced False Negatives and Large Type Coverage

Paper content:
Fine-grained Entity Recognition (FgER) is the task of detecting and classifying entity mentions to a large set of types spanning diverse domains such as biomedical, finance and sports.
We  observe  that  when  the  type  set  spans  several  domains,  detection  of  entity mention becomes a limitation for supervised learning models.
The primary reason being lack  of  dataset  where  entity  boundaries  are  properly  annotated  while  covering  a  large spectrum of entity types.
Our work directly addresses this issue.
We propose Heuristics Allied with Distant Supervision (HAnDS) framework to automatically construct a quality dataset suitable for the FgER task.
HAnDS framework exploits the high interlink among Wikipedia  and  Freebase  in  a  pipelined  manner,  reducing  annotation  errors  introduced by naively using distant supervision approach.
Using HAnDS framework,  we create two datasets, one suitable for building FgER systems recognizing up to 118 entity types based on the FIGER type hierarchy and another for up to 1115 entity types based on the TypeNet hierarchy.
Our extensive empirical experimentation warrants the quality of the generated datasets.
Along with this, we also provide a manually annotated dataset for benchmarking FgER systems.

assistant: text: We initiate a push towards building ER systems to recognize thousands of types by providing a method to automatically construct suitable datasets based on the type hierarchy.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: On the relationship between Normalising Flows and Variational- and Denoising Autoencoders

Paper content:
Normalising Flows (NFs) are a class of likelihood-based generative models that have recently gained popularity.
They are based on the idea of transforming a simple density into that of the data.
We seek to better understand this class of models, and how they compare to previously proposed techniques for generative modeling and unsupervised representation learning.
For this purpose we reinterpret NFs in the framework of Variational Autoencoders (VAEs), and present a new form of VAE that generalises normalising flows.
The new generalised model also reveals a close connection to denoising autoencoders, and we therefore call our model the Variational Denoising Autoencoder (VDAE).
Using our unified model, we systematically examine the model space between flows, variational autoencoders, and denoising autoencoders, in a set of preliminary experiments on the MNIST handwritten digits.
The experiments shed light on the modeling assumptions implicit in these models, and they suggest multiple new directions for future research in this space.

assistant: text: We explore the relationship between Normalising Flows and Variational- and Denoising Autoencoders, and propose a novel model that generalises them.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: The Power of  Semantic Similarity based Soft-Labeling for Generalized Zero-Shot Learning

Paper content:
Zero-Shot Learning (ZSL) is a classification task where some classes referred as unseen classes have no labeled training images.
Instead, we only have side information (or description) about seen and unseen classes, often in the form of semantic or descriptive attributes.
Lack of training images from a set of classes restricts the use of standard classification techniques and losses, including the popular cross-entropy loss.
The key step in tackling ZSL problem is bridging visual to semantic space via learning a nonlinear embedding.
A well established approach is to obtain the semantic representation of the visual information and perform classification in the semantic space.
In this paper, we propose a novel architecture of casting ZSL as a fully connected neural-network with cross-entropy loss to embed visual space to semantic space.
During training in order to introduce unseen visual information to the network, we utilize soft-labeling based on semantic similarities between seen and unseen classes.
To the best of our knowledge, such similarity based soft-labeling is not explored for cross-modal transfer and ZSL.
We evaluate the proposed model on five benchmark datasets for zero-shot learning, AwA1, AwA2, aPY, SUN and CUB datasets, and show that, despite the simplicity, our approach achieves the state-of-the-art performance in Generalized-ZSL setting on all of these datasets and outperforms the state-of-the-art for some datasets.

assistant: text: How to use cross-entropy loss for zero shot learning with soft labeling on unseen classes : a simple and effective solution that achieves state-of-the-art performance on five ZSL benchmark datasets.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Sparse Transformer: Concentrated Attention Through Explicit Selection

Paper content:
Self-attention-based Transformer has demonstrated the state-of-the-art performances in a number of natural language processing tasks.
Self attention is able to model long-term dependencies, but it may suffer from the extraction of irrelevant information in the context.
To tackle the problem, we propose a novel model called Sparse Transformer.
Sparse Transformer is able to improve the concentration of attention on the global context through an explicit selection of the most relevant segments.
Extensive experimental results on a series of natural language processing tasks, including neural machine translation, image captioning, and language modeling, all demonstrate the advantages of Sparse Transformer in model performance.
Sparse Transformer reaches the state-of-the-art performances in the IWSLT 2015 English-to-Vietnamese translation and IWSLT 2014 German-to-English translation.
In addition, we conduct qualitative analysis to account for Sparse Transformer's superior performance.

assistant: text: This work propose Sparse Transformer to improve the concentration of attention on the global context through an explicit selection of the most relevant segments for sequence to sequence learning.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Goal-conditioned Imitation Learning

Paper content:
Designing rewards for Reinforcement Learning (RL) is challenging because it needs to convey the desired task, be efficient to optimize, and be easy to compute.
The latter is particularly problematic when applying RL to robotics, where detecting whether the desired configuration is reached might require considerable supervision and instrumentation.
Furthermore, we are often interested in being able to reach a wide range of configurations, hence setting up a different reward every time might be unpractical.
Methods like Hindsight Experience Replay (HER) have recently shown promise to learn policies able to reach many goals, without the need of a reward.
Unfortunately, without tricks like resetting to points along the trajectory, HER might take a very long time to discover how to reach certain areas of the state-space.
In this work we investigate different approaches to incorporate demonstrations to drastically speed up the convergence to a policy able to reach any goal, also surpassing the performance of an agent trained with other Imitation Learning algorithms.
Furthermore, our method can be used when only trajectories without expert actions are available, which can leverage kinestetic or third person demonstration.

assistant: text: We tackle goal-conditioned tasks by combining Hindsight Experience Replay and Imitation Learning algorithms, showing faster convergence than the first and higher final performance than the second.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Mixture Density Networks Find Viewpoint the Dominant Factor for Accurate Spatial Offset Regression

Paper content:
Offset regression is a standard method for spatial localization in many vision tasks, including human pose estimation, object detection, and instance segmentation.
However, 
if high localization accuracy is crucial for a task, convolutional neural networks will offset regression
usually struggle to deliver.
This can be attributed to the locality of the convolution operation, exacerbated by variance in scale, clutter, and viewpoint.
An even more fundamental issue is the multi-modality of real-world images.
As a consequence, they cannot be approximated adequately using a single mode model.
Instead, we propose to use mixture density networks (MDN) for offset regression, allowing the model to manage various modes efficiently and learning to predict full conditional density of the outputs given the input.
On 2D human pose estimation in the wild, which requires accurate localisation of body keypoints, we show that this yields significant improvement in localization accuracy.
In particular, our experiments reveal viewpoint variation as  the dominant  multi-modal factor.
Further, by carefully initializing MDN parameters, we do not face any instabilities in training, which is known to be a big obstacle for widespread deployment of MDN.
The method can be readily applied to any task with a spatial regression component.
Our findings  highlight the multi-modal nature of real-world vision, and the significance of explicitly accounting for viewpoint variation, at least when spatial localization is concerned.

assistant: text: We use mixture density networks to do full conditional density estimation for spatial offset regression and apply it to the human pose estimation task.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Coloring graph neural networks for node disambiguation

Paper content:
In this paper, we show that a simple coloring scheme can improve, both theoretically and empirically, the expressive power of Message Passing Neural Networks (MPNNs).
More specifically, we introduce a graph neural network called Colored Local Iterative Procedure (CLIP) that uses colors to disambiguate identical node attributes, and show that this representation is a universal approximator of continuous functions on graphs with node attributes.
Our method relies on separability, a key topological characteristic that allows to extend well-chosen neural networks into universal representations.
Finally, we show experimentally that CLIP is capable of capturing structural characteristics that traditional MPNNs fail to distinguish, while being state-of-the-art on benchmark graph classification datasets.

assistant: text: This paper introduces a coloring scheme for node disambiguation in graph neural networks based on separability, proven to be a universal MPNN extension.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Variational autoencoders trained with q-deformed lower bounds

Paper content:
Variational autoencoders (VAEs) have been successful at learning a low-dimensional manifold from high-dimensional data with complex dependencies.
At their core, they consist of a powerful Bayesian probabilistic inference model, to capture the salient features of the data.
In training, they exploit the power of variational inference, by optimizing a lower bound on the model evidence.
The latent representation and the performance of VAEs are heavily influenced by the type of bound used as a cost function.
Significant research work has been carried out into the development of tighter bounds than the original ELBO, to more accurately approximate the true log-likelihood.
By leveraging the q-deformed logarithm in the traditional lower bounds, ELBO and IWAE, and the upper bound CUBO, we bring contributions to this direction of research.
In this proof-of-concept study, we explore different ways of creating these q-deformed bounds that are tighter than the classical ones and we show improvements in the performance of such VAEs on the binarized MNIST dataset.

assistant: text: Using the q-deformed logarithm, we derive tighter bounds than IWAE, to train variational autoencoders.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: 

Paper content:
Neural networks offer high-accuracy solutions to a range of problems, but are computationally costly to run in production systems.
We propose a technique called Deep Learning Approximation to take an already-trained neural network model and build a faster (and almost equally accurate) network by manipulating the network structure and coefficients without requiring re-training or access to the training data.
Speedup is achieved by applying a sequential series of independent optimizations that reduce the floating-point operations (FLOPs) required to perform a forward pass.
An optimal lossy approximation is chosen for each layer by weighing the relative accuracy loss and FLOP reduction.
On PASCAL VOC 2007 with the YOLO network, we show an end-to-end 2x speedup in a network forward pass with a $5$\% drop in mAP that can be re-gained by finetuning, enabling this network (and others like it) to be deployed in compute-constrained systems.
FLOPs for DISPLAYFORM0 An optimal approximation is chosen by calculating the runtime and accuracy loss from all possible Table 1 .
When chaining approximations, R for is the ratio 55 of the final output FLOPs to the FLOPs from W .
A is the product of the accuracy scores for each 56 approximation in the chain, since any error introduced by the first will be carried over to the next.
with the absolute speedup, with the exception of the ResNet50 network, as shown in TAB3 .
Table 61 4 shows that the input parameter p can be chosen based on the desired runtime / accuracy tradeoff.
of an improvement in runtime from DLA (for example ResNet50 in TAB3 ).
Additionally, pushing 64 beyond the 2x speedup observed on YOLO without significant accuracy loss is not possible with

assistant: text: Decompose weights to use fewer FLOPs with SVD
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Multi-objective training of Generative Adversarial Networks with multiple discriminators

Paper content:
Recent literature has demonstrated promising results on the training of Generative Adversarial Networks by employing a set of discriminators, as opposed to the traditional game involving one generator against a single adversary.
Those methods perform single-objective optimization on some simple consolidation of the losses, e.g. an average.
In this work, we revisit the multiple-discriminator approach by framing the simultaneous minimization of losses provided by different models as a multi-objective optimization problem.
Specifically, we evaluate the performance of multiple gradient descent and the hypervolume maximization algorithm on a number of different datasets.
Moreover, we argue that the previously proposed methods and hypervolume maximization can all be seen as variations of multiple gradient descent in which the update direction computation can be done efficiently.
Our results indicate that hypervolume maximization presents a better compromise between sample quality and diversity, and computational cost than previous methods.

assistant: text: We introduce hypervolume maximization for training GANs with multiple discriminators, showing performance improvements in terms of sample quality and diversity.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Sample Efficient Adaptive Text-to-Speech

Paper content:
We present a meta-learning approach for adaptive text-to-speech (TTS) with few data.
During training, we learn a multi-speaker model using a shared conditional WaveNet core and independent learned embeddings for each speaker.
The aim of training is not to produce a neural network with fixed weights, which is then deployed as a TTS system.
Instead, the aim is to produce a network that requires few data at deployment time to rapidly adapt to new speakers.
We introduce and benchmark three strategies:
(i) learning the speaker embedding while keeping the WaveNet core fixed,
(ii) fine-tuning the entire architecture with stochastic gradient descent, and
(iii) predicting the speaker embedding with a trained neural network encoder.
The experiments show that these approaches are successful at adapting the multi-speaker neural network to new speakers, obtaining state-of-the-art results in both sample naturalness and voice similarity with merely a few minutes of audio data from new speakers.

assistant: text: Sample efficient algorithms to adapt a text-to-speech model to a new voice style with the state-of-the-art performance.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Manifold Modeling in Embedded Space: A Perspective for Interpreting "Deep Image Prior"

Paper content:
Deep image prior (DIP), which utilizes a deep convolutional network (ConvNet) structure itself as an image prior, has attracted huge attentions in computer vision community.
It empirically shows the effectiveness of ConvNet structure for various image restoration applications.
However, why the DIP works so well is still unknown, and why convolution operation is essential for image reconstruction or enhancement is not very clear.
In this study, we tackle these questions.
The proposed approach is dividing the convolution into ``delay-embedding'' and ``transformation (\ie encoder-decoder)'', and proposing a simple, but essential, image/tensor modeling method which is closely related to dynamical systems and self-similarity.
The proposed method named as manifold modeling in embedded space (MMES) is implemented by using a novel denoising-auto-encoder in combination with multi-way delay-embedding transform.
In spite of its simplicity, the image/tensor completion and super-resolution results of MMES are quite similar even competitive to DIP in our extensive experiments, and these results would help us for reinterpreting/characterizing the DIP from a perspective of ``low-dimensional patch-manifold prior''.

assistant: text: We propose a new auto-encoder incorporated with multiway delay-embedding transform toward interpreting deep image prior.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: A Training Scheme for the Uncertain Neuromorphic Computing Chips

Paper content:
Uncertainty is a very important feature of the intelligence and helps the brain become a flexible, creative and powerful intelligent system.
The crossbar-based neuromorphic computing chips, in which the computing is mainly performed by analog circuits, have the uncertainty and can be used to imitate the brain.
However, most of the current deep neural networks have not taken the uncertainty of the neuromorphic computing chip into consideration.
Therefore, their performances on the neuromorphic computing chips are not as good as on the original platforms (CPUs/GPUs).
In this work, we proposed the uncertainty adaptation training scheme (UATS) that tells the uncertainty to the neural network in the training process.
The experimental results show that the neural networks can achieve comparable inference performances on the uncertain neuromorphic computing chip compared to the results on the original platforms, and much better than the performances without this training scheme.

assistant: text: A training method that can make deep learning algorithms work better on neuromorphic computing chips with uncertainty