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

Title: Implementing Inductive bias for different navigation tasks through diverse RNN attrractors

Paper content:
Navigation is crucial for animal behavior and is assumed to require an internal representation of the external environment, termed a cognitive map.
The precise form of this representation is often considered to be a metric representation of space.
An internal representation, however, is judged by its contribution to performance on a given task, and may thus vary between different types of navigation tasks.
Here we train a recurrent neural network that controls an agent performing several navigation tasks in a simple environment.
To focus on internal representations, we split learning into a task-agnostic pre-training stage that modifies internal connectivity and a task-specific Q learning stage that controls the network's output.
We show that pre-training shapes the attractor landscape of the networks, leading to either a continuous attractor, discrete attractors or a disordered state.
These structures induce bias onto the Q-Learning phase, leading to a performance pattern across the tasks corresponding to metric and topological regularities.
Our results show that, in recurrent networks, inductive bias takes the form of attractor landscapes -- which can be shaped by pre-training and analyzed using dynamical systems methods.
Furthermore, we demonstrate that non-metric representations are useful for navigation tasks.

assistant: text: This paper explores how pre-training recurrent networks on different navigational objectives confers different benefits for solving downstream tasks, and shows how different pretraining manifests as different dynamical structures in the networks after pre-training.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Robust Domain Randomization for Reinforcement Learning

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

assistant: text: We produce reinforcement learning agents that generalize well to a wide range of environments using a novel regularization technique.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Tandem Blocks in Deep Convolutional Neural Networks

Paper content:
Due to the success of residual networks (resnets) and related architectures, shortcut connections have quickly become standard tools for building convolutional neural networks.
The explanations in the literature for the apparent effectiveness of shortcuts are varied and often contradictory.
We hypothesize that shortcuts work primarily because they act as linear counterparts to nonlinear layers.
We test this hypothesis by using several variations on the standard residual block, with different types of linear connections, to build small (100k--1.2M parameter) image classification networks.
Our experiments show that other kinds of linear connections can be even more effective than the identity shortcuts.
Our results also suggest that the best type of linear connection for a given application may depend on both network width and depth.

assistant: text: This paper investigates the effect of replacing identity skip connections with trainable convolutional skip connections in ResNet and finds that performance improves.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Higher-order Weighted Graph Convolutional Networks

Paper content:
Graph Convolution Network (GCN) has been recognized as one of the most effective graph models for semi-supervised learning, but it extracts merely the first-order or few-order neighborhood information through information propagation, which suffers performance drop-off for deeper structure.
Existing approaches that deal with the higher-order neighbors tend to take advantage of adjacency matrix power.
In this paper, we assume a seemly trivial condition that the higher-order neighborhood information may be similar to that of the first-order neighbors.
Accordingly, we present an unsupervised approach to describe such similarities and learn the weight matrices of higher-order neighbors automatically through Lasso that minimizes the feature loss between the first-order and higher-order neighbors, based on which we formulate the new convolutional filter for GCN to learn the better node representations.
Our model, called higher-order weighted GCN (HWGCN), has achieved the state-of-the-art results on a number of node classification tasks over Cora, Citeseer and Pubmed datasets.

assistant: text: The authors propose a variant of GCN, HWGCN, to consider convolution beyond 1-step neighbors, which is comparable to state-of-the-art methods.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning the Arrow of Time for Problems in Reinforcement Learning

Paper content:
We humans have an innate understanding of the asymmetric progression of time, which we use to efficiently and safely perceive and manipulate our environment.
Drawing inspiration from that, we approach the problem of learning an arrow of time in a Markov (Decision) Process.
We illustrate how a learned arrow of time can capture salient information about the environment, which in turn can be used to measure reachability, detect side-effects and to obtain an intrinsic reward signal.
Finally, we propose a simple yet effective algorithm to parameterize the problem at hand and learn an arrow of time with a function approximator (here, a deep neural network).
Our empirical results span a selection of discrete and continuous environments, and demonstrate for a class of stochastic processes that the learned arrow of time agrees reasonably well with a well known notion of an arrow of time due to Jordan, Kinderlehrer and Otto (1998).

assistant: text: This work proposes the h-potential as a solution to an objective that measures state-transition asymmetry in an MDP.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: ProxSGD: Training Structured Neural Networks under Regularization and Constraints

Paper content:
In this paper, we consider the problem of training neural networks (NN).
To promote a NN with specific structures, we explicitly take into consideration the nonsmooth regularization (such as L1-norm) and constraints (such as interval constraint).
This is formulated as a constrained nonsmooth nonconvex optimization problem, and we propose a convergent proximal-type stochastic gradient descent (Prox-SGD) algorithm.
We show that under properly selected learning rates, momentum eventually resembles the unknown real gradient and thus is crucial in analyzing the convergence.
We establish that with probability 1, every limit point of the sequence generated by the proposed Prox-SGD is a stationary point.
Then the Prox-SGD is tailored to train a sparse neural network and a binary neural network, and the theoretical analysis is also supported by extensive numerical tests.

assistant: text: We propose a convergent proximal-type stochastic gradient descent algorithm for constrained nonsmooth nonconvex optimization problems
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Semantic Hierarchy Emerges in the Deep Generative Representations for Scene Synthesis

Paper content:
Despite the success of Generative Adversarial Networks (GANs) in image synthesis, there lacks enough understanding on what networks have learned inside the deep generative representations and how photo-realistic images are able to be composed from random noises.
In this work, we show that highly-structured semantic hierarchy emerges from the generative representations as the variation factors for synthesizing scenes.
By probing the layer-wise representations with a broad set of visual concepts at different abstraction levels, we are able to quantify the causality between the activations and the semantics occurring in the output image.
Such a quantification identifies the human-understandable variation factors learned by GANs to compose scenes.
The qualitative and quantitative results suggest that the generative representations learned by GAN are specialized to synthesize different hierarchical semantics: the early layers tend to determine the spatial layout and configuration, the middle layers control the categorical objects, and the later layers finally render the scene attributes as well as color scheme.
Identifying such a set of manipulatable latent semantics facilitates semantic scene manipulation.

assistant: text: The paper presents a visually-guided interpretation of activations of the convolution layers in the generator of StyleGAN on layout, scene category, scene attributes, and color.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Deep Evidential Uncertainty

Paper content:
Deterministic neural networks (NNs) are increasingly being deployed in safety critical domains, where calibrated, robust and efficient measures of uncertainty are crucial.
While it is possible to train regression networks to output the parameters of a probability distribution by maximizing a Gaussian likelihood function, the resulting model remains oblivious to the underlying confidence of its predictions.
In this paper, we propose a novel method for training deterministic NNs to not only estimate the desired target but also the associated evidence in support of that target.
We accomplish this by  placing evidential priors over our original Gaussian likelihood function and training our NN to infer the hyperparameters of our evidential distribution.
We impose priors during training such that the model is penalized when its predicted evidence is not aligned with the correct output.
Thus the model estimates not only the probabilistic mean and variance of our target but also the underlying uncertainty associated with each of those parameters.
We observe that our evidential regression method learns well-calibrated measures of uncertainty on various benchmarks, scales to complex computer vision tasks, and is robust to adversarial input perturbations.

assistant: text: This paper proposes a novel approach to estimate the confidence of predictions in a regression setting, opening the door to online applications with fully integrated uncertainty estimates.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: CONTROLLING COVARIATE SHIFT USING EQUILIBRIUM NORMALIZATION OF WEIGHTS

Paper content:
We introduce a new normalization technique that exhibits the fast convergence properties of batch normalization using a transformation of layer weights instead of layer outputs.
The proposed technique keeps the contribution of positive and negative weights to the layer output in equilibrium.
We validate our method on a set of standard benchmarks including CIFAR-10/100, SVHN and ILSVRC 2012 ImageNet.

assistant: text: Introduces a normalization technique, which normalizes the weights of convolutional layers.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Improved Adversarial Image Captioning

Paper content:
In this paper we study image captioning as a conditional GAN training, proposing both a context-aware LSTM captioner and co-attentive discriminator, which enforces semantic alignment between images and captions.
We investigate the viability of two discrete GAN training methods: Self-critical Sequence Training (SCST) and Gumbel Straight-Through (ST) and demonstrate that SCST shows more stable gradient behavior and improved results over Gumbel ST.
Significant progress has been made on the task of generating image descriptions using neural image captioning.
Early systems were traditionally trained using cross-entropy (CE) loss minimization BID6 BID16 .
Later, reinforcement learning techniques BID13 BID9 based on policy gradient methods were introduced to directly optimize metrics such as CIDEr or SPICE BID0 .
Along a similar idea, BID14 introduced Self-critical Sequence Training (SCST), a light-weight variant of REINFORCE, which produced state of the art image captioning results using CIDEr as an optimization metric.
To address the problem of sentence diversity and naturalness, image captioning has been explored in the framework of GANs.
However, due to the discrete nature of text generation, GAN training remains challenging and has been generally tackled either with reinforcement learning techniques BID4 BID12 BID2 or by using Gumbel softmax relaxation BID5 , as in BID15 BID7 .Despite
impressive advances, image captioning is far from being a solved task. It remains
a challenge to satisfactorily bridge the semantic gap between image and captions to produce diverse, creative, and "human-like" captions. Although applying
GANs to image captioning for promoting human-like captions is a very promising direction, the discrete nature of the text generation process makes it challenging to train such systems. The recent work of
BID1 showed that the task of text generation for current discrete GAN models is difficult, often producing unsatisfactory results, and requires therefore new approaches and methods.In this paper, we propose a novel GAN-based framework for image captioning that enables better language composition, more accurate compositional alignment of image and text, and light-weight efficient training of discrete sequence GAN based on SCST.
In summary, we demonstrated that SCST training for discrete GAN is a promissing new approach that outperforms the Gumbel relaxation in terms of training stability and the overall performance.
Moreover, we showed that our context-aware attention gives larger gains as compared to the adaptive sentinel or the traditional visual attention.
Finally, our co-attention model for discriminator compares favorably against the joint embedding architecture.

assistant: text: Image captioning as a conditional GAN training with novel architectures, also study two discrete GAN training methods.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Deep Mining: Detecting Anomalous Patterns in Neural Network Activations with Subset Scanning

Paper content:
This work views neural networks as data generating systems and applies anomalous pattern detection techniques on that data in order to detect when a network is processing a group of anomalous inputs.
Detecting anomalies is a critical component for multiple machine learning problems including detecting the presence of adversarial noise added to inputs.
More broadly, this work is a step towards giving neural networks the ability to detect groups of out-of-distribution samples.
This work introduces ``Subset Scanning methods from the anomalous pattern detection domain to the task of detecting anomalous inputs to neural networks.
Subset Scanning allows us to answer the question: "``Which subset of inputs have larger-than-expected activations at which subset of nodes?
"  Framing the adversarial detection problem this way allows us to identify systematic patterns in the activation space that span multiple adversarially noised images.
Such images are ``"weird together".
Leveraging this common anomalous pattern, we show increased detection power as the proportion of noised images increases in a test set.
Detection power and accuracy results are provided for targeted adversarial noise added to CIFAR-10 images on a 20-layer ResNet using the Basic Iterative Method attack.

assistant: text: The paper proposed a scheme to detect the presence of anomalous inputs based on a "subset scanning" approach to detect anomalous activations in the deep learning network.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: AutoGrow: Automatic Layer Growing in Deep Convolutional Networks

Paper content:
Depth is a key component of Deep Neural Networks (DNNs), however, designing depth is heuristic and requires many human efforts.
We propose AutoGrow to automate depth discovery in DNNs: starting from a shallow seed architecture, AutoGrow grows new layers if the growth improves the accuracy; otherwise, stops growing and thus discovers the depth.
We propose robust growing and stopping policies to generalize to different network architectures and datasets.
Our experiments show that by applying the same policy to different network architectures, AutoGrow can always discover near-optimal depth on various datasets of MNIST, FashionMNIST, SVHN, CIFAR10, CIFAR100 and ImageNet.
For example, in terms of accuracy-computation trade-off, AutoGrow discovers a better depth combination in ResNets than human experts.
Our AutoGrow is efficient.
It discovers depth within similar time of training a single DNN.

assistant: text: A framework to interleave training a shallower network and adding new layers which provides insights into the paradigm of 'growing networks'.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: LatentPoison -- Adversarial Attacks On The Latent Space

Paper content:
Robustness and security of machine learning (ML) systems are intertwined, wherein a non-robust ML system (classifiers, regressors, etc.) can be subject to attacks using a wide variety of exploits.
With the advent of scalable deep learning methodologies, a lot of emphasis has been put on the robustness of supervised, unsupervised and reinforcement learning algorithms.
Here, we study the robustness of the latent space of a deep variational autoencoder (dVAE), an unsupervised generative framework, to show that it is indeed possible to perturb the latent space, flip the class predictions and keep the classification probability approximately equal before and after an attack.
This means that an agent that looks at the outputs of a decoder would remain oblivious to an attack.

assistant: text: Adversarial attacks on the latent space of variational autoencoders to change the semantic meaning of inputs
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Utility Analysis of Network Architectures for 3D Point Cloud Processing

Paper content:
In this paper, we diagnose deep neural networks for 3D point cloud processing to explore the utility of different network architectures.
We propose a number of hypotheses on the effects of specific network architectures on the representation capacity of DNNs.
In order to prove the hypotheses, we design five metrics to diagnose various types of DNNs from the following perspectives, information discarding, information concentration, rotation robustness, adversarial robustness, and neighborhood inconsistency.
We conduct comparative studies based on such metrics to verify the hypotheses, which may shed new lights on the architectural design of neural networks.
Experiments demonstrated the effectiveness of our method.
The code will be released when this paper is accepted.

assistant: text: The paper investigates different neural network architectures for 3D point cloud processing and proposes metrics for adversarial robustness, rotational robustness, and neighborhood consistency.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Improved Adversarial Image Captioning

Paper content:
In this paper we study image captioning as a conditional GAN training, proposing both a context-aware LSTM captioner and co-attentive discriminator, which enforces semantic alignment between images and captions.
We investigate the viability of two discrete GAN training methods: Self-critical Sequence Training (SCST) and Gumbel Straight-Through (ST) and demonstrate that SCST shows more stable gradient behavior and improved results over Gumbel ST.
Significant progress has been made on the task of generating image descriptions using neural image captioning.
Early systems were traditionally trained using cross-entropy (CE) loss minimization BID6 BID16 .
Later, reinforcement learning techniques BID13 BID9 based on policy gradient methods were introduced to directly optimize metrics such as CIDEr or SPICE BID0 .
Along a similar idea, BID14 introduced Self-critical Sequence Training (SCST), a light-weight variant of REINFORCE, which produced state of the art image captioning results using CIDEr as an optimization metric.
To address the problem of sentence diversity and naturalness, image captioning has been explored in the framework of GANs.
However, due to the discrete nature of text generation, GAN training remains challenging and has been generally tackled either with reinforcement learning techniques BID4 BID12 BID2 or by using Gumbel softmax relaxation BID5 , as in BID15 BID7 .Despite
impressive advances, image captioning is far from being a solved task. It remains
a challenge to satisfactorily bridge the semantic gap between image and captions to produce diverse, creative, and "human-like" captions. Although applying
GANs to image captioning for promoting human-like captions is a very promising direction, the discrete nature of the text generation process makes it challenging to train such systems. The recent work of
BID1 showed that the task of text generation for current discrete GAN models is difficult, often producing unsatisfactory results, and requires therefore new approaches and methods.In this paper, we propose a novel GAN-based framework for image captioning that enables better language composition, more accurate compositional alignment of image and text, and light-weight efficient training of discrete sequence GAN based on SCST.
In summary, we demonstrated that SCST training for discrete GAN is a promissing new approach that outperforms the Gumbel relaxation in terms of training stability and the overall performance.
Moreover, we showed that our context-aware attention gives larger gains as compared to the adaptive sentinel or the traditional visual attention.
Finally, our co-attention model for discriminator compares favorably against the joint embedding architecture.

assistant: text: An improved GAN model for image captioning that proposes a context-aware LSTM captioner, introduces a stronger co-attentive discriminator with better performance, and uses SCST for GAN training.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Prediction Under Uncertainty with Error Encoding Networks

Paper content:
In this work we introduce a new framework for performing temporal predictions
in the presence of uncertainty.
It is based on a simple idea of disentangling com-
ponents of the future state which are predictable from those which are inherently
unpredictable, and encoding the unpredictable components into a low-dimensional
latent variable which is fed into the forward model.
Our method uses a simple su-
pervised training objective which is fast and easy to train.
We evaluate it in the
context of video prediction on multiple datasets and show that it is able to consi-
tently generate diverse predictions without the need for alternating minimization
over a latent space or adversarial training.

assistant: text: A simple and easy to train method for multimodal prediction in time series.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Diversity-Sensitive Conditional Generative Adversarial Networks

Paper content:
We propose a simple yet highly effective method that addresses the mode-collapse problem in the Conditional Generative  Adversarial  Network (cGAN).
Although conditional distributions are multi-modal (i.e., having many modes) in practice, most cGAN approaches tend to learn an overly simplified distribution where an input is always mapped to a single output regardless of variations in latent code.
To address such issue, we propose to explicitly regularize the generator to produce diverse outputs depending on latent codes.
The proposed regularization is simple, general, and can be easily integrated into most conditional GAN objectives.
Additionally, explicit regularization on generator allows our method to control a balance between visual quality and diversity.
We demonstrate the effectiveness of our method on three conditional generation tasks: image-to-image translation, image inpainting, and future video prediction.
We show that simple addition of our regularization to existing models leads to surprisingly diverse generations, substantially outperforming the previous approaches for multi-modal conditional generation specifically designed in each individual task.

assistant: text: We propose a simple and general approach that avoids a mode collapse problem in various conditional GANs.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Information in Infinite Ensembles of Infinitely-Wide Neural Networks

Paper content:
In  this  preliminary  work,  we  study  the  generalization  properties  of  infinite  ensembles  of infinitely-wide neural networks.
Amazingly, this model family admits tractable calculations for many information-theoretic quantities.
We report analytical and empirical investigations in the search for signals that correlate with generalization.

assistant: text: Infinite ensembles of infinitely wide neural networks are an interesting model family from an information theoretic perspective.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Point Cloud GAN

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

assistant: text: This paper proposes using GAN to generate 3D point cloud and introduces a sandwiching objective, averaging the upper and lower bound of Wasserstein distance between distributions.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: GENERATING HIGH FIDELITY IMAGES WITH SUBSCALE PIXEL NETWORKS AND MULTIDIMENSIONAL UPSCALING

Paper content:
The unconditional generation of high fidelity images is a longstanding benchmark
for testing the performance of image decoders.
Autoregressive image models
have been able to generate small images unconditionally, but the extension of
these methods to large images where fidelity can be more readily assessed has
remained an open problem.
Among the major challenges are the capacity to encode
the vast previous context and the sheer difficulty of learning a distribution that
preserves both global semantic coherence and exactness of detail.
To address the
former challenge, we propose the Subscale Pixel Network (SPN), a conditional
decoder architecture that generates an image as a sequence of image slices of equal
size.
The SPN compactly captures image-wide spatial dependencies and requires a
fraction of the memory and the computation.
To address the latter challenge, we
propose to use multidimensional upscaling to grow an image in both size and depth
via intermediate stages corresponding to distinct SPNs.
We evaluate SPNs on the
unconditional generation of CelebAHQ of size 256 and of ImageNet from size 32
to 128.
We achieve state-of-the-art likelihood results in multiple settings, set up
new benchmark results in previously unexplored settings and are able to generate
very high fidelity large scale samples on the basis of both datasets.

assistant: text: An architecture utilizing decoder, size-upscaling decoder, and depth-upscaling decoder components to tackle the problem of learning long-range dependencies in images in order to obtain high fidelity images.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Synthesizing realistic neural population activity patterns using Generative Adversarial Networks

Paper content:
The ability to synthesize realistic patterns of neural activity is crucial for studying neural information processing.
Here we used the Generative Adversarial Networks (GANs) framework to simulate the concerted activity of a population of neurons.
We adapted the Wasserstein-GAN variant to facilitate the generation of unconstrained neural population activity patterns while still benefiting from parameter sharing in the temporal domain.
We demonstrate that our proposed GAN, which we termed Spike-GAN, generates spike trains that match accurately the first- and second-order statistics of datasets of tens of neurons and also approximates well their higher-order statistics.
We applied Spike-GAN to a real dataset recorded from salamander retina and showed that it performs as well as state-of-the-art approaches based on the maximum entropy and the dichotomized Gaussian frameworks.
Importantly, Spike-GAN does not require to specify a priori the statistics to be matched by the model, and so constitutes a more flexible method than these alternative approaches.
Finally, we show how to exploit a trained Spike-GAN  to construct 'importance maps' to detect the most relevant statistical structures present in a spike train.
Spike-GAN provides a powerful, easy-to-use technique for generating realistic spiking neural activity and for describing the most relevant features of the large-scale neural population recordings studied in modern systems neuroscience.

assistant: text: Using Wasserstein-GANs to generate realistic neural activity and to detect the most relevant features present in neural population patterns.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Exploring Cellular Protein Localization Through Semantic Image Synthesis

Paper content:
Cell-cell interactions have an integral role in tumorigenesis as they are critical in governing immune responses.
As such, investigating specific cell-cell interactions has the potential to not only expand upon the understanding of tumorigenesis, but also guide clinical management of patient responses to cancer immunotherapies.
A recent imaging technique for exploring cell-cell interactions, multiplexed ion beam imaging by time-of-flight (MIBI-TOF), allows for cells to be quantified in 36 different protein markers at sub-cellular resolutions in situ as high resolution multiplexed images.
To explore the MIBI images, we propose a GAN for multiplexed data with protein specific attention.
By conditioning image generation on cell types, sizes, and neighborhoods through semantic segmentation maps, we are able to observe how these factors affect cell-cell interactions simultaneously in different protein channels.
Furthermore, we design a set of metrics and offer the first insights towards cell spatial orientations, cell protein expressions, and cell neighborhoods.
Our model, cell-cell interaction GAN (CCIGAN), outperforms or matches existing image synthesis methods on all conventional measures and significantly outperforms on biologically motivated metrics.
To our knowledge, we are the first to systematically model multiple cellular protein behaviors and interactions under simulated conditions through image synthesis.

assistant: text: A new method to model the data generated by multiplexed ion beam imaging by time-of-flight (MIBI-TOF) by learning the many-to-many mapping between cell types and protein markers' expression levels.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Adaptive Quantization of Neural Networks

Paper content:
Despite the state-of-the-art accuracy of Deep Neural Networks (DNN) in various classification problems, their deployment onto resource constrained edge computing devices remains challenging due to their large size and complexity.
Several recent studies have reported remarkable results in reducing this complexity through quantization of DNN models.
However, these studies usually do not consider the changes in the loss function when performing quantization, nor do they take the different importances of DNN model parameters to the accuracy into account.
We address these issues in this paper by proposing a new method, called adaptive quantization, which simplifies a trained DNN model by finding a unique, optimal precision for each network parameter such that the increase in loss is minimized.
The optimization problem at the core of this method iteratively uses the loss function gradient to determine an error margin for each parameter and assigns it a precision accordingly.
Since this problem uses linear functions, it is computationally cheap and, as we will show, has a closed-form approximate solution.
Experiments on MNIST, CIFAR, and SVHN datasets showed that the proposed method can achieve near or better than state-of-the-art reduction in model size with similar error rates.
Furthermore, it can achieve compressions close to floating-point model compression methods without loss of accuracy.

assistant: text: An adaptive method for fixed-point quantization of neural networks based on theoretical analysis rather than heuristics.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Retrieving Signals in the Frequency Domain with Deep Complex Extractors

Paper content:
Recent advances have made it possible to create deep complex-valued neural networks.
Despite this progress, the potential power of fully complex intermediate computations and representations has not yet been explored for many challenging learning problems.
Building on recent advances, we propose a novel mechanism for extracting signals in the frequency domain.
As a case study, we perform audio source separation in the Fourier domain.
Our extraction mechanism could be regarded as a local ensembling method that combines a complex-valued convolutional version of Feature-Wise Linear Modulation (FiLM) and a signal averaging operation.
We also introduce a new explicit amplitude and phase-aware loss, which is scale and time invariant, taking into account the complex-valued components of the spectrogram.
Using the Wall Street Journal Dataset, we compare our phase-aware loss to several others that operate both in the time and frequency domains and demonstrate the effectiveness of our proposed signal extraction method and proposed loss.
When operating in the complex-valued frequency domain, our deep complex-valued network substantially outperforms its real-valued counterparts even with half the depth and a third of the parameters.
Our proposed mechanism improves significantly deep complex-valued networks' performance and we demonstrate the usefulness of its regularizing effect.

assistant: text: Contributes a complex-valued convolutional version of the Feature-Wise Linear Modulation which allows parameter optimization and designs a loss which takes into account magnitude and phase.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Natural Language Detectors Emerge in Individual Neurons

Paper content:
Although deep convolutional networks have achieved improved performance in many natural language tasks, they have been treated as black boxes because they are difficult to interpret.
Especially, little is known about how they represent language in their intermediate layers.
In an attempt to understand the representations of deep convolutional networks trained on language tasks, we show that individual units are selectively responsive to specific morphemes, words, and phrases, rather than responding to arbitrary and uninterpretable patterns.
In order to quantitatively analyze such intriguing phenomenon, we propose a concept alignment method based on how units respond to replicated text.
We conduct analyses with different architectures on multiple datasets for classification and translation tasks and provide new insights into how deep models understand natural language.

assistant: text: We show that individual units in CNN representations learned in NLP tasks are selectively responsive to specific natural language concepts.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: LEARNING TO ORGANIZE KNOWLEDGE WITH N-GRAM MACHINES

Paper content:
Deep neural networks (DNNs) had great success on NLP tasks such as language modeling, machine translation and certain question answering (QA) tasks.
However, the success is limited at more knowledge intensive tasks such as QA from a big corpus.
Existing end-to-end deep QA models (Miller et al., 2016; Weston et al., 2014) need to read the entire text after observing the question, and therefore their complexity in responding a question is linear in the text size.
This is prohibitive for practical tasks such as QA from Wikipedia, a novel, or the Web.
We propose to solve this scalability issue by using symbolic meaning representations, which can be indexed and retrieved efficiently with complexity that is independent of the text size.
More specifically, we use sequence-to-sequence models to encode knowledge symbolically and generate programs to answer questions from the encoded knowledge.
We apply our approach, called the N-Gram Machine (NGM), to the bAbI tasks (Weston et al., 2015) and a special version of them (“life-long bAbI”) which has stories of up to 10 million sentences.
Our experiments show that NGM can successfully solve both of these tasks accurately and efficiently.
Unlike fully differentiable memory models, NGM’s time complexity and answering quality are not affected by the story length.
The whole system of NGM is trained end-to-end with REINFORCE (Williams, 1992).
To avoid high variance in gradient estimation, which is typical in discrete latent variable models, we use beam search instead of sampling.
To tackle the exponentially large search space, we use a stabilized auto-encoding objective and a structure tweak procedure to iteratively reduce and refine the search space.

assistant: text: We propose a framework that learns to encode knowledge symbolically and generate programs to reason about the encoded knowledge.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Beyond Cost-to-go Estimates in Situated Temporal Planning

Paper content:
Heuristic search research often deals with finding algorithms for offline planning which aim to minimize the number of expanded nodes or planning time.
In online planning, algorithms for real-time search or deadline-aware search have been considered before.
However, in this paper, we are interested in the problem of {\em situated temporal planning} in which an agent's plan can depend on exogenous events in the external world, and thus it becomes important to take the passage of time into account during the planning process.
Previous work on situated temporal planning has proposed simple pruning strategies, as well as complex schemes for a simplified version of the associated metareasoning problem.
In this paper, we propose a simple metareasoning technique,  called the crude greedy scheme, which can be applied in a situated temporal planner.
Our empirical evaluation shows that the crude greedy scheme outperforms standard heuristic search based on cost-to-go estimates.

assistant: text: Metareasoning in a Situated Temporal Planner
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Generating Multi-Agent Trajectories using Programmatic Weak Supervision

Paper content:
We study the problem of training sequential generative models for capturing coordinated multi-agent trajectory behavior, such as  offensive basketball gameplay.
When modeling such settings, it is often beneficial to design hierarchical models that can capture long-term coordination using intermediate variables.
Furthermore, these intermediate variables should capture interesting high-level behavioral semantics in an interpretable and manipulable way.
We present a hierarchical framework that can effectively learn such sequential generative models.
Our approach is inspired by recent work on leveraging programmatically produced weak labels, which we extend to the spatiotemporal regime.
In addition to synthetic settings, we show how to instantiate our framework to effectively model complex interactions between basketball players and generate realistic multi-agent trajectories of basketball gameplay over long time periods.
We validate our approach using both quantitative and qualitative evaluations, including a user study comparison conducted with professional sports analysts.

assistant: text: We blend deep generative models with programmatic weak supervision to generate coordinated multi-agent trajectories of significantly higher quality than previous baselines.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Lifelong Word Embedding via Meta-Learning

Paper content:
Learning high-quality word embeddings is of significant importance in achieving better performance in many down-stream learning tasks.
On one hand, traditional word embeddings are trained on a large scale corpus for general-purpose tasks, which are often sub-optimal for many domain-specific tasks.
On the other hand, many domain-specific tasks do not have a large enough domain corpus to obtain high-quality embeddings.
We observe that domains are not isolated and a small domain corpus can leverage the learned knowledge from many past domains to augment that corpus in order to generate high-quality embeddings.
In this paper, we formulate the learning of word embeddings as a lifelong learning process.
Given knowledge learned from many previous domains and a small new domain corpus, the proposed method can effectively generate new domain embeddings by leveraging a simple but effective algorithm and a meta-learner, where the meta-learner is able to provide word context similarity information at the domain-level.
Experimental results demonstrate that the proposed method can effectively learn new domain embeddings from a small corpus and past domain knowledges\footnote{We will release the code after final revisions.}.
We
also demonstrate that general-purpose embeddings trained from a large scale corpus are sub-optimal in domain-specific tasks.

assistant: text: Presents a lifelong learning method for learning word embeddings.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Consistency-based anomaly detection with adaptive multiple-hypotheses predictions

Paper content:
In one-class-learning tasks, only the normal case can be modeled with data, whereas the variation of all possible anomalies is too large to be described sufficiently by samples.
Thus, due to the lack of representative data, the wide-spread discriminative approaches cannot cover such learning tasks, and rather generative models, which attempt to learn the input density of the normal cases, are used.
However, generative models suffer from a large input dimensionality (as in images) and are typically inefficient learners.
We propose to learn the data distribution more efficiently with a multi-hypotheses autoencoder.
Moreover, the model is criticized by a discriminator, which prevents artificial data modes not supported by data, and which enforces diversity across hypotheses.
This consistency-based anomaly detection (ConAD) framework allows the reliable identification of outof- distribution samples.
For anomaly detection on CIFAR-10, it yields up to 3.9% points improvement over previously reported results.
On a real anomaly detection task, the approach reduces the error of the baseline models from 6.8% to 1.5%.

assistant: text: We propose an anomaly-detection approach that combines modeling the foreground class via multiple local densities with adversarial training.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: CONTROLLING COVARIATE SHIFT USING EQUILIBRIUM NORMALIZATION OF WEIGHTS

Paper content:
We introduce a new normalization technique that exhibits the fast convergence properties of batch normalization using a transformation of layer weights instead of layer outputs.
The proposed technique keeps the contribution of positive and negative weights to the layer output in equilibrium.
We validate our method on a set of standard benchmarks including CIFAR-10/100, SVHN and ILSVRC 2012 ImageNet.
The introduction of normalizing layers to neural networks has in no small part contributed to the deep learning revolution in machine learning.
The most successful of these techniques in the image classification domain is the batch normalization (BatchNorm) layer BID4 , which works by normalizing the univariate first and second order statistics between layers.Batchnorm has seen near universal adoption in image classification tasks due to its surprisingly multifaceted benefits.
Compared to an unnormalized network, its has been widely observed that using batch norm empirically results in:• Stability over a wide range of step sizes • Faster convergence (particularly with larger step sizes)• Improved generalizationThe multiple effects of BatchNorm make it both hard to replace and hard to analyze.
In this paper we introduce Equilibrium Normalization (EquiNorm), a normalization that works in weight space and still uses a form of batch statistics unlike previous weight space approaches.
EquiNorm results in very rapid convergence, even more so than BatchNorm, however as we will show in our experiments, this also results in a tendency to overfit.
When combined with additional regularisation, EquiNorm can significantly outperform BatchNorm, which benefits less from this additional regularisation.

assistant: text: Introduces a normalization technique, which normalizes the weights of convolutional layers.