There is some debate about these conflicting results #REFR , whether they arise from Urbano et al.'s use of parametric distributions, but Urbano et al.

Simulation allowed them to control whether the null hypothesis was true or not, and compute actual error rates and power. Parapar et al.

#OTHEREFR 's method simulates new runs for the same topic while Urbano et al.

#OTHEREFR 's approach simulates new topics for the same system. Based on their findings, Urbano et al.

#OTHEREFR recommended the use of the -test while Parapar et al.

#OTHEREFR recommended the use of the sign and Wilcoxon tests.

#OTHEREFR argue that their methods would favor the Wilcoxon test if it was appropriate.

We treat the topic evaluation scores of a system as observations of a random variable following Sakai #OTHEREFR .

Therefore, we adopt the method proposed by Urbano and Nagler #OTHEREFR for simulation.

Significance Testing For Large Search And Recommendation Experiments

Abstract: A number of information retrieval studies have been done to assess which statistical techniques are appropriate for comparing systems. However, these studies are focused on TREC-style experiments, which typically have fewer than 100 topics. There is no similar line of work for large search and recommendation experiments; such studies typically have thousands of topics or users and much sparser relevance judgements, so it is not clear if recommendations for analyzing traditional TREC experiments apply to these settings. In this paper, we empirically study the behavior of significance tests with large search and recommendation evaluation data. Our results show that the Wilcoxon and Sign tests show significantly higher Type-1 error rates for large sample sizes than the bootstrap, randomization and t-tests, which were more consistent with the expected error rate. While the statistical tests displayed differences in their power for smaller sample sizes, they showed no difference in their power for large sample sizes. We recommend the sign and Wilcoxon tests should not be used to analyze large scale evaluation results. Our result demonstrate that with Top-recommendation and large search evaluation data, most tests would have a 100% chance of finding statistically significant results. Therefore, the effect size should be used to determine practical or scientific significance.

Using Score Distributions To Compare Statistical Significance Tests For Information Retrieval Evaluation

Abstract: Preprint of our Journal of the Association for Information Science and Technology (JASIST) paper

conflicting results
parametric distributions


To this end, we formally introduce the concept of Probabilistic Reachable Set as follows #REFR .

It contains all possible reachable states x k at time k, for any possible states x k−1 at time k − 1, any possible control inputs u k−1 without violating actuator constraints, any model parameters θ k−1 , and any disturbances w k−1 .

The goal of performing reachability analysis is to figure out a reachable set X k .

However, since the sets U k−1 , Θ, W, and X 0 are not limited to bounded sets, the reachable set X k may be unbounded.

Hence, it's impossible to find a bounded set in which the state x k is guaranteed to lie.

Instead, we expect to find a bounded set such that the probability of the state x k lying in the bounded set is greater than a confidence level.

Definition 2 (Probabilistic Reachable Set).

At time k, a bounded set is defined to be a probabilistic reachable setX k of the dynamic system eq. (1) at confidence level α if and only if

A probabilistic reachable set is a set of possible states that a system can reach with a certain probability. Note thatX k may not exist.

Convex Approximation For Probabilistic Reachable Set Under Data-Driven Uncertainties

Abstract: This paper is proposed to efficiently provide a convex approximation for the probabilistic reachable set of a dynamic system in the face of uncertainties. When the uncertainties are not limited to bounded ones, it may be impossible to find a bounded reachable set of the system. Instead, we turn to find a probabilistic reachable set that bounds system states with confidence. A data-driven approach of Kernel Density Estimator (KDE) accelerated by Fast Fourier Transform (FFT) is customized to model the uncertainties and obtain the probabilistic reachable set efficiently. However, the irregular or non-convex shape of the probabilistic reachable set refrains it from practice. For the sake of real applications, we formulate an optimization problem as Mixed Integer Nonlinear Programming (MINLP) whose solution accounts for an optimal n-sided convex polygon to approximate the probabilistic reachable set. A heuristic algorithm is then developed to solve the MINLP efficiently while ensuring accuracy. The results of comprehensive case studies demonstrate the near-optimality, accuracy, efficiency, and robustness enjoyed by the proposed algorithm. The benefits of this work pave the way for its promising applications to safety-critical real-time motion planning of uncertain systems.

Recursively Feasible Stochastic Model Predictive Control Using Indirect Feedback

Abstract: We present a stochastic model predictive control (MPC) method for linear discrete-time systems subject to possibly unbounded and correlated additive stochastic disturbance sequences. Chance constraints are treated in analogy to robust MPC using the concept of probabilistic reachable sets for constraint tightening. We introduce an initialization of each MPC iteration which is always recursively feasibility and thereby allows that chance constraint satisfaction for the closed-loop system can readily be shown. Under an i.i.d. zero mean assumption on the additive disturbance, we furthermore provide an average asymptotic performance bound. Two examples illustrate the approach, highlighting feedback properties of the novel initialization scheme, as well as the inclusion of time-varying, correlated disturbances in a building control setting.


RL algorithms have multiple hyperparameters that require careful tuning as it is a core aspect of obtaining the state-of-the-art performance #REFR .

Reinforcement Learning (RL) is a sub-field of Machine Learning with a great success in applications such as self-driving cars, industry automation, among many others #OTHEREFR .

In RL, autonomous agents learn through trial-and-error how to find optimal solutions to a problem #OTHEREFR .

The search for the best hyperparameter configuration is a sequential decision process in which initial values are set, and later adjusted, through a mixture of intuition and trial-and-error, to optimise an observed performance to maximise the accuracy or minimise the loss #OTHEREFR .

Hyperparameter Optimisation (HPO) often requires expensive manual or automated hyperparameter searches in order to perform properly on an application domain #OTHEREFR .

However, a noticeable limitation is the high cost related to algorithm evaluation, which makes the tuning process highly inefficient, computational expensive, and commonly adds extra algorithm developing overheads to the RL agent decision-making processes #OTHEREFR .

The full behaviour of complex RL systems often only emerges during operation.

They thus need to be monitored at runtime to check that they adhere to their requirements #OTHEREFR .

A Framework For History-Aware Hyperparameter Optimisation In Reinforcement Learning

Abstract: A Reinforcement Learning (RL) system depends on a set of initial conditions (hyperparameters) that affect the system's performance. However, defining a good choice of hyperparameters is a challenging problem. Hyperparameter tuning often requires manual or automated searches to find optimal values. Nonetheless, a noticeable limitation is the high cost of algorithm evaluation for complex models, making the tuning process computationally expensive and time-consuming. In this paper, we propose a framework based on integrating complex event processing and temporal models, to alleviate these trade-offs. Through this combination, it is possible to gain insights about a running RL system efficiently and unobtrusively based on data stream monitoring and to create abstract representations that allow reasoning about the historical behaviour of the RL system. The obtained knowledge is exploited to provide feedback to the RL system for optimising its hyperparameters while making effective use of parallel resources. We introduce a novel history-aware epsilon-greedy logic for hyperparameter optimisation that instead of using static hyperparameters that are kept fixed for the whole training, adjusts the hyperparameters at runtime based on the analysis of the agent's performance over time windows in a single agent's lifetime. We tested the proposed approach in a 5G mobile communications case study that uses DQN, a variant of RL, for its decision-making. Our experiments demonstrated the effects of hyperparameter tuning using history on training stability and reward values. The encouraging results show that the proposed history-aware framework significantly improved performance compared to traditional hyperparameter tuning approaches.

Hyp-Rl : Hyperparameter Optimization By Reinforcement Learning

Abstract: Hyperparameter tuning is an omnipresent problem in machine learning as it is an integral aspect of obtaining the state-of-the-art performance for any model. Most often, hyperparameters are optimized just by training a model on a grid of possible hyperparameter values and taking the one that performs best on a validation sample (grid search). More recently, methods have been introduced that build a so-called surrogate model that predicts the validation loss for a specific hyperparameter setting, model and dataset and then sequentially select the next hyperparameter to test, based on a heuristic function of the expected value and the uncertainty of the surrogate model called acquisition function (sequential model-based Bayesian optimization, SMBO). ::: In this paper we model the hyperparameter optimization problem as a sequential decision problem, which hyperparameter to test next, and address it with reinforcement learning. This way our model does not have to rely on a heuristic acquisition function like SMBO, but can learn which hyperparameters to test next based on the subsequent reduction in validation loss they will eventually lead to, either because they yield good models themselves or because they allow the hyperparameter selection policy to build a better surrogate model that is able to choose better hyperparameters later on. Experiments on a large battery of 50 data sets demonstrate that our method outperforms the state-of-the-art approaches for hyperparameter learning.


The Liar dataset #REFR provides even more labels: 'true', 'mostly-true', 'half-true', 'barely-true', 'false', and 'pants-fire'.

If possible, the authors' recommendation for a threshold is followed.

The original format of every dataset is shown in Table 3 .

For instance, the Kaggle Jigsaw data #OTHEREFR is labeled on a scale from 0 to 1.

The authors recommend using a 0.5 threshold to binarize the label, which we follow for MBIB. Also, we collapse multi-categorical labels into two categories.

For instance, for the political bias task, 'right' and 'left' are combined into 'biased' #OTHEREFR .

The first four labels are combined into a single 'true' label and the last two into one 'false' label.

Even though social media-specific elements such as hashtags or emoticons are used in related areas such as sentiment analysis #OTHEREFR , we remove them to not deviate further from a news format.

As an additional step, we enrich the FakeNewsNet dataset by scraping the tweets or articles referred to by the Tweet IDs given in the original resource #OTHEREFR .

Every decision with regards to the unified format is furthermore detailed in the MBIB repository.

Introducing Mbib -The First Media Bias Identification Benchmark Task And Dataset Collection

Abstract: Although media bias detection is a complex multi-task problem, there is, to date, no unified benchmark grouping these evaluation tasks. We introduce the Media Bias Identification Benchmark (MBIB), a comprehensive benchmark that groups different types of media bias (e.g., linguistic, cognitive, political) under a common framework to test how prospective detection techniques generalize. After reviewing 115 datasets, we select nine tasks and carefully propose 22 associated datasets for evaluating media bias detection techniques. We evaluate MBIB using state-of-the-art Transformer techniques (e.g., T5, BART). Our results suggest that while hate speech, racial bias, and gender bias are easier to detect, models struggle to handle certain bias types, e.g., cognitive and political bias. However, our results show that no single technique can outperform all the others significantly. We also find an uneven distribution of research interest and resource allocation to the individual tasks in media bias. A unified benchmark encourages the development of more robust systems and shifts the current paradigm in media bias detection evaluation towards solutions that tackle not one but multiple media bias types simultaneously.

"Liar, Liar Pants On Fire": A New Benchmark Dataset For Fake News Detection

Abstract: Automatic fake news detection is a challenging problem in deception detection, and it has tremendous real-world political and social impacts. However, statistical approaches to combating fake news has been dramatically limited by the lack of labeled benchmark datasets. In this paper, we present liar: a new, publicly available dataset for fake news detection. We collected a decade-long, 12.8K manually labeled short statements in various contexts from PolitiFact.com, which provides detailed analysis report and links to source documents for each case. This dataset can be used for fact-checking research as well. Notably, this new dataset is an order of magnitude larger than previously largest public fake news datasets of similar type. Empirically, we investigate automatic fake news detection based on surface-level linguistic patterns. We have designed a novel, hybrid convolutional neural network to integrate meta-data with text. We show that this hybrid approach can improve a text-only deep learning model.


Each graph convolution block applies the Chebyshev spectral graph convolution (ChebConv) #REFR followed by batch normalization and a leaky rectified linear unit (LeakyReLU).

In this study, we consider two network architectures, SGCN and MGCN, to investigate the effect of network architecture on the mesh inpainting performance using a self-prior.

As its name indicates, SGCN performs the graph convolution defined on the mesh in the original resolution.

SGCN consists of 13 graph convolution blocks and one vertex-wise fully connected layer.

In contrast, MGCN employs an hourglass-type encoderdecoder network architecture. The encoder and decoder consist of several computation blocks.

Each encoder block consists of five ChebConv layers with average mesh pooling, while each decoder block consists of five ChebConv layers with mesh unpooling.

The last decoder block is followed by a single fully connected layer.

The filter size of each ChebConv is determined by the order K of the Chebyshev polynomial.

We use K = 3, corresponding to the convolution over vertices in 2-ring neighbors.

Learning Self-Prior For Mesh Inpainting Using Self-Supervised Graph Convolutional Networks

Abstract: This study presents a self-prior-based mesh inpainting framework that requires only an incomplete mesh as input, without the need for any training datasets. Additionally, our method maintains the polygonal mesh format throughout the inpainting process without converting the shape format to an intermediate, such as a voxel grid, a point cloud, or an implicit function, which are typically considered easier for deep neural networks to process. To achieve this goal, we introduce two graph convolutional networks (GCNs): single-resolution GCN (SGCN) and multi-resolution GCN (MGCN), both trained in a self-supervised manner. Our approach refines a watertight mesh obtained from the initial hole filling to generate a completed output mesh. Specifically, we train the GCNs to deform an oversmoothed version of the input mesh into the expected completed shape. To supervise the GCNs for accurate vertex displacements, despite the unknown correct displacements at real holes, we utilize multiple sets of meshes with several connected regions marked as fake holes. The correct displacements are known for vertices in these fake holes, enabling network training with loss functions that assess the accuracy of displacement vectors estimated by the GCNs. We demonstrate that our method outperforms traditional dataset-independent approaches and exhibits greater robustness compared to other deep-learning-based methods for shapes that less frequently appear in shape datasets.

Convolutional Neural Networks On Graphs With Fast Localized Spectral Filtering

Abstract: In this work, we are interested in generalizing convolutional neural networks (CNNs) from low-dimensional regular grids, where image, video and speech are represented, to high-dimensional irregular domains, such as social networks, brain connectomes or words' embedding, represented by graphs. We present a formulation of CNNs in the context of spectral graph theory, which provides the necessary mathematical background and efficient numerical schemes to design fast localized convolutional filters on graphs. Importantly, the proposed technique offers the same linear computational complexity and constant learning complexity as classical CNNs, while being universal to any graph structure. Experiments on MNIST and 20NEWS demonstrate the ability of this novel deep learning system to learn local, stationary, and compositional features on graphs.


For instance, BoostClean #REFR deals with the error repair task as a statistical boosting problem.

In fact, the above presented techniques, such as HoloClean, OpenRefine, and BARAN, do not consider the requirements imposed by the downstream ML applications.

They tend to improve the data quality regardless of where and how the data comes from or how the data will be consumed.

Therefore, a new set of techniques and tools has been emerged which strives to jointly optimize the cleaning and modeling tasks.

In other words, these ML-oriented methods focus on selecting the optimal repair candidates with the objective of improving the performance of specific predictive models.

Accordingly, these methods assume the availability of repair candidates from other ML-agnostic methods.

Specifically, it composes a set of weak learners into a strong learner.

To generate the weak learners, BoostClean iteratively selects a pair of detection and repair methods, before applying them to the training set to derive a new model.

ActiveClean #OTHEREFR is another ML-oriented method, principally employed for models with convex loss functions.

It formulates the data cleaning task as a stochastic gradient descent problem.

Initially, it trains a model on a dirty training set, where such a model is to be iteratively updated until reaching a global minimum.

Autocure: Automated Tabular Data Curation Technique For Ml Pipelines

Abstract: Machine learning algorithms have become increasingly prevalent in multiple domains, such as autonomous driving, healthcare, and finance. In such domains, data preparation remains a significant challenge in developing accurate models, requiring significant expertise and time investment to search the huge search space of well-suited data curation and transformation tools. To address this challenge, we present AutoCure, a novel and configuration-free data curation pipeline that improves the quality of tabular data. Unlike traditional data curation methods, AutoCure synthetically enhances the density of the clean data fraction through an adaptive ensemble-based error detection method and a data augmentation module. In practice, AutoCure can be integrated with open source tools, e.g., Auto-sklearn, H2O, and TPOT, to promote the democratization of machine learning. As a proof of concept, we provide a comparative evaluation of AutoCure against 28 combinations of traditional data curation tools, demonstrating superior performance and predictive accuracy without user intervention. Our evaluation shows that AutoCure is an effective approach to automating data preparation and improving the accuracy of machine learning models.

Boostclean: Automated Error Detection And Repair For Machine Learning

Abstract: Predictive models based on machine learning can be highly sensitive to data error. Training data are often combined with a variety of different sources, each susceptible to different types of inconsistencies, and new data streams during prediction time, the model may encounter previously unseen inconsistencies. An important class of such inconsistencies is domain value violations that occur when an attribute value is outside of an allowed domain. We explore automatically detecting and repairing such violations by leveraging the often available clean test labels to determine whether a given detection and repair combination will improve model accuracy. We present BoostClean which automatically selects an ensemble of error detection and repair combinations using statistical boosting. BoostClean selects this ensemble from an extensible library that is pre-populated general detection functions, including a novel detector based on the Word2Vec deep learning model, which detects errors across a diverse set of domains. Our evaluation on a collection of 12 datasets from Kaggle, the UCI repository, real-world data analyses, and production datasets that show that Boost- Clean can increase absolute prediction accuracy by up to 9% over the best non-ensembled alternatives. Our optimizations including parallelism, materialization, and indexing techniques show a 22.2x end-to-end speedup on a 16-core machine.


Conversely, BertScore #REFR ) calculated with concatenated predictions and references captures semantic similarity, but its token-wise matching with contextual representations built in one encoding pass prevents evaluating the semantic similarity among individual phrases.

Desiderata: semantically similar phrases should be credited; matching should be at phrase level.

Precision and recall with exact matching can capture saliency and coverage, while they struggle at dealing with semantic similarity.

To enjoy the advantage of both, we introduce phrase-level semantic-based matching and define semantic precision (SemP ), recall (SemR), and F1 (SemF 1) as follows:

SemP (P, Y) = p∈P max y∈Y (1(sim(p,y)>α)•sim(p,y))

Kpeval: Towards Fine-Grained Semantic-Based Evaluation Of Keyphrase Extraction And Generation Systems

Abstract: Despite the significant advancements in keyphrase extraction and keyphrase generation methods, the predominant approach for evaluation only relies on exact matching with human references and disregards referencefree attributes. This scheme fails to recognize systems that generate keyphrases that are semantically equivalent to the references or keyphrases that have practical utility. To better understand the strengths and weaknesses of different keyphrase systems, we propose a comprehensive evaluation framework consisting of six critical dimensions: naturalness, faithfulness, saliency, coverage, diversity, and utility. For each dimension, we discuss the desiderata and design semantic-based metrics that align with the evaluation objectives. Rigorous meta-evaluation studies demonstrate that our evaluation strategy correlates better with human preferences compared to a range of previously used metrics. Using this framework, we re-evaluate 18 keyphrase systems and further discover that (1) the best model differs in different dimensions, with pre-trained language models achieving the best in most dimensions; (2) the utility in downstream tasks does not always correlate well with reference-based metrics; and (3) large language models exhibit a strong performance in reference-free evaluation.

Bertscore: Evaluating Text Generation With Bert

Abstract: We propose BERTScore, an automatic evaluation metric for text generation. Analogously to common metrics, BERTScore computes a similarity score for each token in the candidate sentence with each token in the reference sentence. However, instead of exact matches, we compute token similarity using contextual embeddings. We evaluate using the outputs of 363 machine translation and image captioning systems. BERTScore correlates better with human judgments and provides stronger model selection performance than existing metrics. Finally, we use an adversarial paraphrase detection task and show that BERTScore is more robust to challenging examples compared to existing metrics.


One challenge with MetaTroll under this continual learning evaluation is that at test time it needs to know which adapter to use -information that most other systems do not require as they don't have campaign-specific parameters (exception: AdBERT and GPT-3 #REFR ).

This is a more realistic setting for a troll detection system, as it should continually adapt to new campaigns that appear over time.

But in this setting it will suffer from catastrophic forgetting #OTHEREFR , where after adapting to newer campaigns its performance to classify older campaigns will degrade.

To simulate this continual learning setting, we next evaluate the troll detection models on a past campaign after it has been adapted for a number of campaigns in sequence.

For example, a system is first adapted to GRU-2020-NATO (G), and then to IRA-2020-Russia (I), Uganda-2021-NRM (U) and China-2021-Xinjiang (C) in sequence (denoted as G→I→U→C).

We then test the system using trolls using the past campaigns, i.e. G, I and U.

We experiment a simple approach to solve this: have MetaTroll classify a user using all its adapters, and select the outcome that has the highest probability.

#OTHEREFR We present (5-shot) troll classification results under this continual learning setting in Table 3 .

AdBERT and GPT-3 suffers little catastrophic forgetting, as they have campaign-specific parameters or prompts (they are unaffected by continual learning as their base model is unchanged), although their performance is only marginally above random chance in the first place.

MetaTroll is the clear winner here, with only <5% accuracy degradation over time.

#OTHEREFR In contrast, the meta-learning methods and few-shot classifiers suffer from catastrophic forgetting and their performance on older campaigns drops substantially (e.g.

Metatroll: Few-Shot Detection Of State-Sponsored Trolls With Transformer Adapters

Abstract: State-sponsored trolls are the main actors of influence campaigns on social media and automatic troll detection is important to combat misinformation at scale. Existing troll detection models are developed based on training data for known campaigns (e.g. the influence campaign by Russia's Internet Research Agency on the 2016 US Election), and they fall short when dealing with novel campaigns with new targets. We propose MetaTroll, a text-based troll detection model based on the meta-learning framework that enables high portability and parameter-efficient adaptation to new campaigns using only a handful of labelled samples for few-shot transfer. We introduce campaign-specific transformer adapters to MetaTroll to "memorise" campaign-specific knowledge so as to tackle catastrophic forgetting, where a model "forgets" how to detect trolls from older campaigns due to continual adaptation. Our experiments demonstrate that MetaTroll substantially outperforms baselines and state-of-the-art few-shot text classification models. Lastly, we explore simple approaches to extend MetaTroll to multilingual and multimodal detection. Source code for MetaTroll is available at: https://github.com/ltian678/metatroll-code.git CCS CONCEPTS • Computing methodologies → Natural language processing; Information extraction.

Generalization Through Memorization: Nearest Neighbor Language Models

Abstract: We introduce $k$NN-LMs, which extend a pre-trained neural language model (LM) by linearly interpolating it with a $k$-nearest neighbors ($k$NN) model. The nearest neighbors are computed according to distance in the pre-trained LM embedding space, and can be drawn from any text collection, including the original LM training data. Applying this transformation to a strong Wikitext-103 LM, with neighbors drawn from the original training set, our $k$NN-LM achieves a new state-of-the-art perplexity of 15.79 -- a 2.9 point improvement with no additional training. We also show that this approach has implications for efficiently scaling up to larger training sets and allows for effective domain adaptation, by simply varying the nearest neighbor datastore, again without further training. Qualitatively, the model is particularly helpful in predicting rare patterns, such as factual knowledge. Together, these results strongly suggest that learning similarity between sequences of text is easier than predicting the next word, and that nearest neighbor search is an effective approach for language modeling in the long tail.


Learning 3D Compact Scene Representations

Neural radiance fields #REFR are capable of learning a 3D representation that can reconstruct a volumetric 3D scene representation from a set of images.

Voxel-based methods #OTHEREFR speed up the learning process by explicitly storing the scene properties (e.g., density, color and feature) in its voxel grids.

We leverage Direct Voxel Grid Optimization (DVGO) #OTHEREFR as our backbone for 3D compact representation for its fast speed.

DVGO stores the learned density and color properties in its grid cells.

The rendering of multi-view images is by interpolating through the voxel grids to get the density and color for each sampled point along each sampled ray, and integrating the colors based on the rendering alpha weights calculated from densities according to quadrature rule #OTHEREFR .

The model is trained by minimizing the L2 loss between the rendered multi-view images and the ground-truth multi-view images.

3D Concept Learning And Reasoning From Multi-View Images

Abstract: Figure 1. An exemplar scene with multi-view images and question-answer pairs of our 3DMV-VQA dataset. 3DMV-VQA contains four question types: concept, counting, relation, comparison. Orange words denote semantic concepts; blue words denote the relations.

Nerf: Representing Scenes As Neural Radiance Fields For View Synthesis

Abstract: We present a method that achieves state-of-the-art results for synthesizing novel views of complex scenes by optimizing an underlying continuous volumetric scene function using a sparse set of input views. Our algorithm represents a scene using a fully-connected (non-convolutional) deep network, whose input is a single continuous 5D coordinate (spatial location $(x,y,z)$ and viewing direction $(\theta, \phi)$) and whose output is the volume density and view-dependent emitted radiance at that spatial location. We synthesize views by querying 5D coordinates along camera rays and use classic volume rendering techniques to project the output colors and densities into an image. Because volume rendering is naturally differentiable, the only input required to optimize our representation is a set of images with known camera poses. We describe how to effectively optimize neural radiance fields to render photorealistic novel views of scenes with complicated geometry and appearance, and demonstrate results that outperform prior work on neural rendering and view synthesis. View synthesis results are best viewed as videos, so we urge readers to view our supplementary video for convincing comparisons.

3D representation
Neural radiance fields


HJR is also, however, infamous for being a burdensome computational approach because of its dependency on spatial gradient approximations, making it sensitive to the curse of dimensionality #REFR .

The technical rigor involved in planning for success while simultaneously avoiding failure tends to force methods to sacrifice guarantees (e.g.

datadriven methods where success is higher but failure is too) or feasibility (e.g.

differential inclusion where solutions are often over-conservative or burdensome to solve).

Among these approaches, Hamilton Jacobi reachability (HJR) is well known for being a robust approach to optimal control and safe path planning #OTHEREFR .

It has taken seat as the golden standard in robotics, autonomous driving and other stochastic control because of its connection to the fundamental theory underlying differential games #OTHEREFR , #OTHEREFR which describe how to optimally posture a system to withstand antagonistic or stochastic disturbances #OTHEREFR .

If this theory could be extended to higher dimensional systems, engineering efforts in diverse domains, particularly in medicine, finance and other large systems, could make strides where simpler (dimension-robust) controllers are unable to overcome stochasticity.

Toward this end, several works have been put forth, including set-based propagation, often called the method of zonotopes #OTHEREFR , to over-approximate the Hamilton Jacobi reachable states with linear systems #OTHEREFR , #OTHEREFR and in some special classes of nonlinear systems #OTHEREFR .

The only shortcomings with these methods are that they don't inherently provide an optimal controller and also tend to be overly-conservative.

Another direction has been pursued in the face of this dimensionality obstacle: the method of decomposition and Sharpless, Shinde, Kim, and Herbert are with University of California, San Diego. Chow is with the University of California, Riverside.

{wsharpless, nshinde, mak009, sherbert}@ucsd.edu, yattinc@ucr.edu This work is supported by ONR YIP N00014-22-1-2292. system reduction for HJR.

Koopman-Hopf Hamilton-Jacobi Reachability And Control

Abstract: The Hopf formula for Hamilton-Jacobi Reachability analysis has been proposed for solving viscosity solutions of high-dimensional differential games as a space-parallelizeable method. In exchange, however, a complex, potentially nonconvex optimization problem must be solved, limiting its application to linear time-varying systems. With the intent of solving Hamilton-Jacobi backwards reachable sets (BRS) and their corresponding online controllers, we pair the Hopf solution with Koopman theory, which can linearize high-dimensional nonlinear systems. We find that this is a viable method for approximating the BRS and performs better than local linearizations. Furthermore, we construct a Koopman-Hopf controller for robustly driving a 10-dimensional, nonlinear, stochastic, glycolysis model and find that it significantly outcompetes both stochastic and game-theoretic Koopman-based model predictive controllers against stochastic disturbance.

Hamilton-Jacobi Reachability: A Brief Overview And Recent Advances

Abstract: Hamilton-Jacobi (HJ) reachability analysis is an important formal verification method for guaranteeing performance and safety properties of dynamical systems; it has been applied to many small-scale systems in the past decade. Its advantages include compatibility with general nonlinear system dynamics, formal treatment of bounded disturbances, and the availability of well-developed numerical tools. The main challenge is addressing its exponential computational complexity with respect to the number of state variables. In this tutorial, we present an overview of basic HJ reachability theory and provide instructions for using the most recent numerical tools, including an efficient GPU-parallelized implementation of a Level Set Toolbox for computing reachable sets. In addition, we review some of the current work in high-dimensional HJ reachability to show how the dimensionality challenge can be alleviated via various general theoretical and application-specific insights.


For the transformer encoder, we simply adopt the same hyperparameters of BERT-base #REFR encoder, i.e., 12 layers, 12 attention heads, 768 hidden size, and 3,072 dimensions for the feed forward layer. Hyperparameters.

Thus, we attempt to analyse the noise process by observing the standard derivation of the cumulative matrix. A higher std.

indicates a more sparse matrix and hence a lower transition probability to other coordinate tokens. As shown in Fig.

6 , our schedule presents a gentler noising process and a more stable convergence state compared to the original linear schedule (as suggested by a higher std.

at the beginning of the forward process, and a lower std. at the very end of the process). Denoising Model. We present the model architecture as in Fig. 7 .

We embed the input sequence, input timestep, and positions with 768, 128, and 768 dimensions respectively. The dropout rate is set as 0.1.

We train our model using AdamW optimizer [30] with lr = 0.00004, betas = (0.9, 0.999), and zero weight decay.

We also apply an exponential moving average (EMA) over model parameters with a rate of 0.9999. We set the batch size as 64.

For RICO #OTHEREFR , we train the model with 2 V100 GPUs for 175,000 steps to achieve the best results; and for PublayNet #OTHEREFR , we train the model with 4 V100 GPUs for 350,000 steps.

For the schedule of type tokens, we set T = 160 in Eq. #OTHEREFR .

For the schedule of coordinate tokens, we set T =T = 160, g = 12.4, h = 2.48, and = 0.0001 in β t = g/(T − t + ) h .

Layoutdiffusion: Improving Graphic Layout Generation By Discrete Diffusion Probabilistic Models

Abstract: Creating graphic layouts is a fundamental step in graphic designs. In this work, we present a novel generative model named LayoutDiffusion for automatic layout generation. As layout is typically represented as a sequence of discrete tokens, LayoutDiffusion models layout generation as a discrete denoising diffusion process. It learns to reverse a mild forward process, in which layouts become increasingly chaotic with the growth of forward steps and layouts in the neighboring steps do not differ too much. Designing such a mild forward process is however very challenging as layout has both categorical attributes and ordinal attributes. To tackle the challenge, we summarize three critical factors for achieving a mild forward process for the layout, i.e., legality, coordinate proximity and type disruption. Based on the factors, we propose a block-wise transition matrix coupled with a piece-wise linear noise schedule. Experiments on RICO and PubLayNet datasets show that Layout-Diffusion outperforms state-of-the-art approaches significantly. Moreover, it enables two conditional layout generation tasks in a plug-and-play manner without retraining and achieves better performance than existing methods.

Bert: Pre-Training Of Deep Bidirectional Transformers For Language Understanding

Abstract: We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models, BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications. ::: BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5% (7.7% point absolute improvement), MultiNLI accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement).


Yet, the formulations of the original NeRF #REFR and its followup variants assume that images were acquired in clear air, i.e., in a medium that does not scatter or absorb light in a significant manner and that the acquired image is composed solely of the object radiance.

The pioneering work of Mildenhall et al.

#OTHEREFR on Neural Radiance Fields (NeRFs) has tremendously advanced the field of Neural Rendering, due to its flexibility and unprecedented quality of synthesized images.

The NeRF formulation is based on volumetric rendering equations that take into account sampled points along 3D rays.

Assuming a clear air environment, an implicit assumption, which is often enforced explicitly with dedicated loss components #OTHEREFR , is that a single opaque (high density) object is encountered per ray, with zero density between the camera and the object.

In stark contrast to clear air case, when the medium is absorbing and / or scattering (e.g., haze, fog, smog, and all aquatic habitats), the volume rendering equation has a true volumetric meaning, as the entire volume, and not only the object, contributes to image intensity.

As the NeRF model estimates color and density at every point of a scene, it lends itself perfectly to general volumetric rendering, given that the appropriate rendering model is used.

Here, we bridge this gap with SeaThru-NeRF, a framework that incorporates a rendering model that takes into account scattering media.

Seathru-Nerf: Neural Radiance Fields In Scattering Media

Abstract: for Marine Sciences in Eilat sea-thru-nerf.github.io Figure 1. NeRFs have not yet tackled scenes in which the medium strongly influences the appearances of objects, as in the case of underwater imagery. By incorporating a scattering image formation model into the NeRF rendering equations, we are able to separate the scene into 'clean' and backscatter components. Consequently, we can render photorealistic novel-views with or without the participating medium, in the latter case recovering colors as if the image was taken in clear air. Results on the Curaçao scene: A RAW image (left) is brightened and white balanced (WB) for visualization, showing more detail, while areas further from the camera (top-right corner) are occluded and attenuated by severe backscatter-which is effectively removed in our restored image. Please zoom-in to observe the details.


Parametric methods, such as autoregres-sive models and Markov models #REFR , assume a specific distribution for the data and can achieve higher accuracy but require more computational resources.

Change point detection is a fundamental technique for community detection and identifying the formation of echo chambers #OTHEREFR , which can amplify bias and increase misinformation in online social networks #OTHEREFR .

By integrating changes in temporal patterns into segregation models, we can better understand and ultimately mitigate the effects of echo chambers on online social networks.

Change point information is also useful in social learning setups #OTHEREFR , where risk-averse agents adjust their estimation of a varying network state.

Existing change point detection approaches can be categorized into three main groups: non-parametric methods, parametric methods, and Bayesian methods.

Nonparametric methods, such as cumulative sum (CUSUM) #OTHEREFR and sliding window methods #OTHEREFR , do not assume any specific distribution for the data and can handle both abrupt and gradual changes.

Bayesian methods, such as Bayesian change point analysis #OTHEREFR , use probabilistic models for the prior of the change point and the observation likelihood to estimate the change points and can handle uncertainty and missing data.

However, detecting changes in social networks is a challenging problem due to the large size of social networks and their complex dynamics.

Additionally, the non-Euclidean nature of networks presents a challenge because traditional statistical tools developed for scalar and vector data are inadequate.

Therefore, there is a need for efficient and effective methods to detect changes in social networks.

Fréchet mean and variance provide a method for calculating mean and variance for metric space-valued random variables, which allows us to examine statistical data for data items located in abstract spaces without algebraic structure and operations such as networks.

Fréchet Statistics Based Change Point Detection In Dynamic Social Networks

Abstract: This paper proposes a method to detect change points in dynamic social networks using Fréchet statistics. We address two main questions: (1) what metric can quantify the distances between graph Laplacians in a dynamic network and enable efficient computation, and (2) how can the Fréchet statistics be extended to detect multiple change points while maintaining the significance level of the hypothesis test? Our solution defines a metric space for graph Laplacians using the Log-Euclidean metric, enabling a closed-form formula for Fréchet mean and variance. We present a framework for change point detection using Fréchet statistics and extend it to multiple change points with binary segmentation. The proposed algorithm uses incremental computation for Fréchet mean and variance to improve efficiency and is validated on simulated and two real-world datasets, namely the UCI message dataset and the Enron email dataset.

Selective Review Of Offline Change Point Detection Methods

Abstract: This article presents a selective survey of algorithms for the offline detection of multiple change points in multivariate time series. A general yet structuring methodological strategy is adopted to organize this vast body of work. More precisely, detection algorithms considered in this review are characterized by three elements: a cost function, a search method and a constraint on the number of changes. Each of those elements is described, reviewed and discussed separately. Implementations of the main algorithms described in this article are provided within a Python package called ruptures.


G Comparison With Other Denoiser-Based Defense

For DefenseGAN, which is originally designed to defend against adversarial images by finding the optimal noise that generates the most similar image to the adversarial counterpart, we adopt it to the audio domain, choosing WaveGAN #REFR as the GAN model in this pipeline.

We compare AudioPure with DefenseGAN #OTHEREFR and Joint Adversarial Finetuning #OTHEREFR .

We train a WaveGAN on the SC09 dataset for 100 epochs, using the Adam optimizer with lr = 1e − 3, β 1 = 0.5, and β 2 = 0.9.

For Joint Adversarial Fine-tuning, we follow the setting of #OTHEREFR

(2022) , using a Conv-TasNet #OTHEREFR as the denoiser. And like Joshi et al.

(2022), we craft an offline adversarial SC09 dataset against the pretrained classifier by using L-inf PGD-100 attacks with = 0.002 (denoted as OffAdv-SC09).

Then we train a Conv-TasNet model on OffAdv-SC09 for 30 epochs to get the pretrained denoiser.

Defending Against Adversarial Audio Via Diffusion Model

Abstract: Deep learning models have been widely used in commercial acoustic systems in recent years. However, adversarial audio examples can cause abnormal behaviors for those acoustic systems, while being hard for humans to perceive. Various methods, such as transformation-based defenses and adversarial training, have been proposed to protect acoustic systems from adversarial attacks, but they are less effective against adaptive attacks. Furthermore, directly applying the methods from the image domain can lead to suboptimal results because of the unique properties of audio data. In this paper, we propose an adversarial purification-based defense pipeline, AudioPure, for acoustic systems via offthe-shelf diffusion models. Taking advantage of the strong generation ability of diffusion models, AudioPure first adds a small amount of noise to the adversarial audio and then runs the reverse sampling step to purify the noisy audio and recover clean audio. AudioPure is a plug-and-play method that can be directly applied to any pretrained classifier without any fine-tuning or retraining. We conduct extensive experiments on speech command recognition task to evaluate the robustness of AudioPure. Our method is effective against diverse adversarial attacks (e.g. L 2 or L ∞-norm). It outperforms the existing methods under both strong adaptive white-box and black-box attacks bounded by L 2 or L ∞norm (up to +20% in robust accuracy). Besides, we also evaluate the certified robustness for perturbations bounded by L 2-norm via randomized smoothing. Our pipeline achieves a higher certified accuracy than baselines. Code is available at https://github.com/cychomatica/AudioPure. * work done during the internship at ASU.

Adversarial Audio Synthesis

Abstract: Audio signals are sampled at high temporal resolutions, and learning to synthesize audio requires capturing structure across a range of timescales. Generative adversarial networks (GANs) have seen wide success at generating images that are both locally and globally coherent, but they have seen little application to audio generation. In this paper we introduce WaveGAN, a first attempt at applying GANs to unsupervised synthesis of raw-waveform audio. WaveGAN is capable of synthesizing one second slices of audio waveforms with global coherence, suitable for sound effect generation. Our experiments demonstrate that, without labels, WaveGAN learns to produce intelligible words when trained on a small-vocabulary speech dataset, and can also synthesize audio from other domains such as drums, bird vocalizations, and piano. We compare WaveGAN to a method which applies GANs designed for image generation on image-like audio feature representations, finding both approaches to be promising.


If, additionally, grasps are constrained to a top-down direction, the surface the objects rest on hinders the gripper from tilting the object while approaching it, which generally improves grasp success rates substantially #REFR .

4-DoF grasp samplers restrict the approach direction of the grasps to be perpendicular to a specific grasping plane #OTHEREFR - #OTHEREFR . As such, these methods are constrained by construction.

One of the obvious benefits of constraining grasps to 4-DoF instead of 6 is that it restricts the space of possible grasps, which facilitates learning.

However, if grasps are generated from a camera plane that is not top-down, which is the case in this work, grasp success deteriorates drastically #OTHEREFR .

Therefore, we do not restrict the approach direction by construction and instead propose constraining it to any subset of SO(3).

Gonet: An Approach-Constrained Generative Grasp Sampling Network

Abstract: Constraining the approach direction of grasps is important when picking objects in confined spaces, such as when emptying a shelf. Yet, such capabilities are not available in state-of-the-art data-driven grasp sampling methods that sample grasps all around the object. In this work, we address the specific problem of training approach-constrained datadriven grasp samplers and how to generate good grasping directions automatically. Our solution is GoNet: a generative grasp sampler that can constrain the grasp approach direction to lie close to a specified direction. This is achieved by discretizing SO(3) into bins and training GoNet to generate grasps from those bins. At run-time, the bin aligning with the second largest principal component of the observed point cloud is selected. GoNet is benchmarked against GraspNet, a state-ofthe-art unconstrained grasp sampler, in an unconfined grasping experiment in simulation and on an unconfined and confined grasping experiment in the real world. The results demonstrate that GoNet achieves higher success-over-coverage in simulation and a 12%-18% higher success rate in real-world table-picking and shelf-picking tasks than the baseline.

Beyond Top-Grasps Through Scene Completion

Abstract: Current end-to-end grasp planning methods propose grasps in the order of (milli)seconds that attain high grasp success rates on a diverse set of objects, but often by constraining the workspace to top-grasps. In this work, we present a method that allows end-to-end top grasp planning methods to generate full six-degree-of-freedom grasps using a single RGB-D view as input. This is achieved by estimating the complete shape of the object to be grasped, then simulating different viewpoints of the object, passing the simulated viewpoints to an end-to-end grasp generation method, and finally executing the overall best grasp. The method was experimentally validated on a Franka Emika Panda by comparing 429 grasps generated by the state-of-the-art Fully Convolutional Grasp Quality CNN, both on simulated and real camera viewpoints. The results show statistically significant improvements in terms of grasp success rate when using simulated viewpoints over real camera viewpoints, especially when the real camera viewpoint is angled.


Moreover, internal representations of neural networks can be harnessed to generate concept activation vectors #REFR .

However, in the context of end-to-end trained neural networks without explicit feature engineering we also have an input space that is much less interpretable than engineered features.

That leads to feature attribution methods coming short in interpretability as saliency maps lack semantic concepts in complex input spaces.

Moreover, global insights across whole datasets are desired to explain the intertwined system of data and trained model.

To overcome this limitation, we can evaluate the impact of the occurence of particular semantic concepts in the input signal, a method named concept influence #OTHEREFR .

Alternative methods to evaluate the impact of semantic concepts on the model output include the inpainting or masking of specific concepts and quantifying the change of output of the neural network [Williford et al. 2020] .

Gaze events like fixations and saccades are main descriptive concepts of eye tracking research #OTHEREFR

2011] and thus are natural candidates for semantic concepts in gaze signals.

By evaluating the concept influence of these gaze concepts across entire datasets we generate insights on both model and data.

Prior eye gaze related research either uses descriptive input features on machine learning models #OTHEREFR

2016] or is limited to statistical analysis #OTHEREFR

Bridging The Gap: Gaze Events As Interpretable Concepts To Explain Deep Neural Sequence Models

Abstract: Recent work in XAI for eye tracking data has evaluated the suitability of feature attribution methods to explain the output of deep neural sequence models for the task of oculomotric biometric identification. These methods provide saliency maps to highlight important input features of a specific eye gaze sequence. However, to date, its localization analysis has been lacking a quantitative approach across entire datasets. In this work, we employ established gaze event detection algorithms for fixations and saccades and quantitatively evaluate the impact of these events by determining their concept influence. Input features that belong to saccades are shown to be substantially more important than features that belong to fixations. By dissecting saccade events into sub-events, we are able to show that gaze samples that are close to the saccadic peak velocity are most influential. We further investigate the effect of event properties like saccadic amplitude or fixational dispersion on the resulting concept influence.

Interpretability Beyond Feature Attribution: Quantitative Testing With Concept Activation Vectors (Tcav)

Abstract: The interpretation of deep learning models is a challenge due to their size, complexity, and often opaque internal state. In addition, many systems, such as image classifiers, operate on low-level features rather than high-level concepts. To address these challenges, we introduce Concept Activation Vectors (CAVs), which provide an interpretation of a neural net's internal state in terms of human-friendly concepts. The key idea is to view the high-dimensional internal state of a neural net as an aid, not an obstacle. We show how to use CAVs as part of a technique, Testing with CAVs (TCAV), that uses directional derivatives to quantify the degree to which a user-defined concept is important to a classification result--for example, how sensitive a prediction of "zebra" is to the presence of stripes. Using the domain of image classification as a testing ground, we describe how CAVs may be used to explore hypotheses and generate insights for a standard image classification network as well as a medical application.


Building on the idea, a higher order ALE Discontinuous Galerkin (DG) method was designed by #REFR .

This approach can be efficient if the number of remeshing required are very low, since the simulation is fully ALE and free from interpolation errors.

The approach described above is sometimes also known as remap ALE methods or indirect ALE methods.

Remap ALE schemes have been used extensively in some problems and some of the recent work on the topic can be found in #OTHEREFR .

For large deformations, the number of required remeshing increase and this can be both costly and result in poor accuracy due to the solution transfer step.

Another approach to get around this problem was presented in #OTHEREFR , where the connectivity of the moving mesh is dynamically regenerated via a moving unstructured but conforming Voronoi tessellation of the domain.

In these methods, a new mesh is generated at every time step, a correspondence is generated between the elements in the two time steps, and finally a ALE DG method is solved on the mesh, where the space-time information is gleaned from the correspondence information.

In the above method, there is a possibility of non-regular ("crazy") finite elements appearing in the mesh which is taken care of using the techniques in #OTHEREFR .

Due to no inherent need to remap the solutions in these methods, these methods are often referred to as direct ALE DG methods.

Since there is no remapping of solutions needed, the methods do not suffer from the interpolation error, however, it requires a mesh to be generated at every time step and corresponding connection information to be determined which can be costly.

There is a third approach which was first outlined in #OTHEREFR .

Arbitrary Lagrangian-Eulerian Discontinuous Galerkin Methods With Application To Compressible Flows

High Order Direct Arbitrary-Lagrangian-Eulerian Schemes On Moving Voronoi Meshes With Topology Changes

Abstract: We present a new family of very high order accurate direct Arbitrary-Lagrangian-Eulerian (ALE) Finite Volume (FV) and Discontinuous Galerkin (DG) schemes for the solution of nonlinear hyperbolic PDE systems on moving Voronoi meshes that are regenerated at each time step and which explicitly allow topology changes in time. The Voronoi tessellations are obtained from a set of generator points that move with the local fluid velocity. We employ an AREPO-type approach, which rapidly rebuilds a new high quality mesh exploiting the previous one, but \textit{rearranging} the element shapes and neighbors in order to guarantee that the mesh evolution is robust even for vortex flows and for very long computational times. The old and new Voronoi elements associated to the same generator point are connected in space--time to construct closed space-time control volumes, whose bottom and top faces may be polygons with a different number of sides. We also need to incorporate some degenerate space--time sliver elements, which are needed in order to fill the space-time holes that arise because of the topology changes. The final ALE FV-DG scheme is obtained by a novel redesign of the high order accurate fully discrete direct ALE schemes of Boscheri and Dumbser, which have been extended here to general moving Voronoi meshes and space--time sliver elements, and it is based on the integration over arbitrary shaped closed space-time control volumes. Numerical convergence studies as well as a large set of benchmark problems for hydrodynamics and magnetohydrodynamics (MHD) demonstrate the accuracy and robustness of the method. Our numerical results clearly show that the new combination of very high order schemes with regenerated meshes that allow topology changes in each time step lead to substantial improvements over the existing state of the art in direct ALE methods.


Overall, the modern sequence modeling neural networks from Section 2.2.2 were seldom used for candidate retrieval in these systems, either because they were used in a way that did not scale to millions of songs, or led to underperforming results with respect to alternatives #REFR .

#OTHEREFR analyzed key insights from the challenge, explaining that many teams leveraged:

• Ensemble architectures: several teams combined several APC models of the form : P × S → R, each of them providing different "candidate" songs to extend playlists.

• Or, two-stage architectures: a first simple model rapidly scored all songs from S, retrieving a subset of "candidate" songs, several orders of magnitude smaller than the original song set.

Then, some more sophisticated model(s) re-ranked candidate songs to improve APC recommendations.

The performance of such two-stage strategies was highly dependent on the quality of candidate retrieval models #OTHEREFR .

When considering both candidate selection models of ensemble/two-stage architectures, as well as single-stage architectures directly scoring all songs, most approaches leveraged simpler and faster non-parametric nearest neighbor models, applied to various CF-based song/playlist embedding representations learned using models from Section 2.2.1.

In summary, most teams favored simpler and more scalable methods than the ones presented in Section 2.2.2 despite their promising performances on smaller datasets.

We believe this challenge highlighted the current discrepancy between the complexity and evaluation procedure of APC models proposed in the recent scientific literature, and the simpler ones successfully used for large-scale applications.

In particular, in the context of a global questioning of the ability of deep learning to actually improve recommender systems #OTHEREFR , we believe that research on APC would benefit from a formal characterization of the requirements expected from suitable solutions for large-scale APC applications, which we provide in the remainder of this paper.

A Scalable Framework For Automatic Playlist Continuation On Music Streaming Services

Abstract: Music streaming services often aim to recommend songs for users to extend the playlists they have created on these services. However, extending playlists while preserving their musical characteristics and matching user preferences remains a challenging task, commonly referred to as Automatic Playlist Continuation (APC). Besides, while these services often need to select the best songs to recommend in real-time and among large catalogs with millions of candidates, recent research on APC mainly focused on models with few scalability guarantees and evaluated on relatively small datasets. In this paper, we introduce a general framework to build scalable yet effective APC models for large-scale applications. Based on a represent-then-aggregate strategy, it ensures scalability by design while remaining flexible enough to incorporate a wide range of representation learning and sequence modeling techniques, e.g., based on Transformers. We demonstrate the relevance of this framework through in-depth experimental validation on Spotify's Million Playlist Dataset (MPD), the largest public dataset for APC. We also describe how, in 2022, we successfully leveraged this framework to improve APC in production on Deezer. We report results from a large-scale online A/B test on this service, emphasizing the practical impact of our approach in such a real-world application.

An Analysis Of Approaches Taken In The Acm Recsys Challenge 2018 For Automatic Music Playlist Continuation

Abstract: The ACM Recommender Systems Challenge 2018 focused on the task of automatic music playlist continuation, which is a form of the more general task of sequential recommendation. Given a playlist of arbitrary length with some additional meta-data, the task was to recommend up to 500 tracks that fit the target characteristics of the original playlist. For the RecSys Challenge, Spotify released a dataset of one million user-generated playlists. Participants could compete in two tracks, i.e., main and creative tracks. Participants in the main track were only allowed to use the provided training set, however, in the creative track, the use of external public sources was permitted. In total, 113 teams submitted 1,228 runs to the main track; 33 teams submitted 239 runs to the creative track. The highest performing team in the main track achieved an R-precision of 0.2241, an NDCG of 0.3946, and an average number of recommended songs clicks of 1.784. In the creative track, an R-precision of 0.2233, an NDCG of 0.3939, and a click rate of 1.785 was obtained by the best team. This article provides an overview of the challenge, including motivation, task definition, dataset description, and evaluation. We further report and analyze the results obtained by the top-performing teams in each track and explore the approaches taken by the winners. We finally summarize our key findings, discuss generalizability of approaches and results to domains other than music, and list the open avenues and possible future directions in the area of automatic playlist continuation.


Since the teacher policies are trained in the same environment for the same embodiment, we can use policy distillation #REFR , by adding a regularization term to encourage the output of the agent's policy to be similar to that of the teachers'.

The setup is the same as for training the soccer teacher, except now episodes terminate only when either the agent or the opponent scores.

When the agent is on the ground, out of bounds, or in the goal penalty area, it receives a fixed penalty per timestep and all positive reward components are ignored.

For instance, if the agent is on the ground when a goal is scored, then it receives a zero for the scoring reward component.

At the beginning of an episode, the agent is initialized either laying on the ground on its front, on its back, or in a default standing pose, with equal probability.

Distillation: Given the soccer and get-up teachers, the agent must learn to transition smoothly between these two skills and improve upon their robustness.

This approach is related to prior work that regularizes a student policy to a common shared policy across tasks #OTHEREFR or a default policy that receives limited state information #OTHEREFR , as well as work on kickstarting #OTHEREFR and reusing learned skills for humanoid soccer in simulation (Liu et al., 2022) .

Unlike most prior work, in our setting the teacher policies are useful in mutually exclusive sets of states: the get-up teacher is only useful in states where the agent is on the ground, and the soccer teacher is only useful in states where the agent is upright.

Thus the agent should only be regularized to one teacher at a time, depending on which state it is in.

Based on this domain-specific knowledge, we split the training objective into two components-one that combines the soccer training objective J ( ) with KL regularization to the soccer teacher and one that combines it with KL regularization to the get-up teacher :

Learning Agile Soccer Skills For A Bipedal Robot With Deep Reinforcement Learning

Abstract: We investigate whether Deep Reinforcement Learning (Deep RL) is able to synthesize sophisticated and safe movement skills for a low-cost, miniature humanoid robot that can be composed into complex behavioral strategies in dynamic environments. We used Deep RL to train a humanoid robot with 20 actuated joints to play a simplified one-versus-one (1v1) soccer game. We first trained individual skills in isolation and then composed those skills end-to-end in a self-play setting. The resulting policy exhibits robust and dynamic movement skills such as rapid fall recovery, walking, turning, kicking and more; and transitions between them in a smooth, stable, and efficient manner-well beyond what is intuitively expected from the robot. The agents also developed a basic strategic understanding of the game, and learned, for instance, to anticipate ball movements and to block opponent shots. The full range of behaviors emerged from a small set of simple rewards. Our agents were trained in simulation and transferred to real robots zero-shot. We found that a combination of sufficiently high-frequency control, targeted dynamics randomization, and perturbations during training in simulation enabled good-quality transfer, despite significant unmodeled effects and variations across robot instances. Although the robots are inherently fragile, minor hardware modifications together with basic regularization of the behavior during training led the robots to learn safe and effective movements while still performing in a dynamic and agile way. Indeed, even though the agents were optimized for scoring, in experiments they walked 156 % faster, took 63 % less time to get up, and kicked 24 % faster than a scripted baseline, while efficiently combining the skills to achieve the longer term objectives. Examples of the emergent behaviors and full 1v1 matches are available on the supplementary website: https://sites.google.com/view/op3-soccer.

Actor-Mimic: Deep Multitask And Transfer Reinforcement Learning

Abstract: The ability to act in multiple environments and transfer previous knowledge to new situations can be considered a critical aspect of any intelligent agent. Towards this goal, we define a novel method of multitask and transfer learning that enables an autonomous agent to learn how to behave in multiple tasks simultaneously, and then generalize its knowledge to new domains. This method, termed "Actor-Mimic", exploits the use of deep reinforcement learning and model compression techniques to train a single policy network that learns how to act in a set of distinct tasks by using the guidance of several expert teachers. We then show that the representations learnt by the deep policy network are capable of generalizing to new tasks with no prior expert guidance, speeding up learning in novel environments. Although our method can in general be applied to a wide range of problems, we use Atari games as a testing environment to demonstrate these methods.


We train the feature grid and the 2D CNN decoder simultaneously, with the supervision of both RGB images and their bilinearly up-sampled feature maps extracted from ReLU3_1 layer of pre-trained VGG #REFR .

The training of our model is divided into the feature grid training stage and the stylization training stage, the former is trained with the target of novel view synthesis, and the latter is trained with the target of style transfer. Feature grid training stage (First stage).

We first learn the feature grid 3D representation for the novel view synthesis task, in preparation for performing feature transformation for style transfer.

By aligning the VGG features with the feature grid, the reconstructed features acquire semantic information.

We use density grid pre-trained solely on RGB images since the supervising feature maps are not strictly multi-view consistent.

The training objective is the mean square error (MSE) between the predicted and ground truth feature maps and RGB images.

Following #OTHEREFR , we use perceptual loss #OTHEREFR as additional supervision to increase reconstructed image quality. The overall loss function is:

L grid = r∈R F (r) − F (r) 2 2 + Î R − I R 2 2 + l∈lp F l (Î R ) − F l (I R ) 2 2 , (10)

Stylerf: Zero-Shot 3D Style Transfer Of Neural Radiance Fields

Abstract: 3D style transfer aims to render stylized novel views of a 3D scene with multi-view consistency. However, most existing work suffers from a three-way dilemma over accurate geometry reconstruction, high-quality stylization, and being generalizable to arbitrary new styles. We propose StyleRF (Style Radiance Fields), an innovative 3D style transfer technique that resolves the three-way dilemma by performing style transformation within the feature space of a radiance field. StyleRF employs an explicit grid of high-level features to represent 3D scenes, with which highfidelity geometry can be reliably restored via volume rendering. In addition, it transforms the grid features according to the reference style which directly leads to highquality zero-shot style transfer. StyleRF consists of two innovative designs. The first is sampling-invariant content transformation that makes the transformation invariant to the holistic statistics of the sampled 3D points and accordingly ensures multi-view consistency. The second is deferred style transformation of 2D feature maps which is equivalent to the transformation of 3D points but greatly reduces memory footprint without degrading multi-view consistency. Extensive experiments show that StyleRF achieves superior 3D stylization quality with precise geometry reconstruction and it can generalize to various new styles in a zero-shot manner. Project website: https://kunhaoliu.github.io/StyleRF/

Very Deep Convolutional Networks For Large-Scale Image Recognition

Abstract: In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3x3) convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respectively. We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results. We have made our two best-performing ConvNet models publicly available to facilitate further research on the use of deep visual representations in computer vision.


We crawl all this information for future exploitation in improving student modeling as well as other relevant topics such as adaptive learning recommendation #REFR .

For each student, we sort their exercise records in time order and collect the timestamp to build an interaction sequence.

Specifically, thanks to the collection of the standard answer, we can automatically assign the performance score of each behavior.

We further consider the Half Right scene that a student gives a partially correct answer in a multiple choice question and classify the scores into three levels: 0-Wrong, 1-Half Right, 2-Right.

Researchers can decide whether to reduce it to a dichotomy when employing our dataset.

(4) Associated Resources and Behaviors: Except for the exercising information and behaviors, there are other types of relevant features, such as the course videos that are in the same chapters, video-watching behaviors, and the course descriptions.

Furthermore, as MOOCCubeX provides the concept set of these resources, we preserve them as a distant supervision dictionary for subsequent annotation.

Moocradar: A Fine-Grained And Multi-Aspect Knowledge Repository For Improving Cognitive Student Modeling In Moocs

Abstract: Student modeling, the task of inferring a student's learning characteristics through their interactions with coursework, is a fundamental issue in intelligent education. Although the recent attempts from knowledge tracing and cognitive diagnosis propose several promising directions for improving the usability and effectiveness of current models, the existing public datasets are still insufficient to meet the need for these potential solutions due to their ignorance of complete exercising contexts, fine-grained concepts, and cognitive labels. In this paper, we present MoocRadar, a fine-grained, multi-aspect knowledge repository consisting of 2, 513 exercise questions, 5, 600 knowledge concepts, and over 12 million behavioral records. Specifically, we propose a framework to guarantee a high-quality and comprehensive annotation of fine-grained concepts and cognitive labels. The statistical and experimental results indicate that our dataset provides the basis for the future improvements of existing methods. Moreover, to support the convenient usage for researchers, we release a set of tools for data querying,

Exploiting Cognitive Structure For Adaptive Learning

Abstract: Adaptive learning, also known as adaptive teaching, relies on learning path recommendation, which sequentially recommends personalized learning items (e.g., lectures, exercises) to satisfy the unique needs of each learner. Although it is well known that modeling the cognitive structure including knowledge level of learners and knowledge structure (e.g., the prerequisite relations) of learning items is important for learning path recommendation, existing methods for adaptive learning often separately focus on either knowledge levels of learners or knowledge structure of learning items. To fully exploit the multifaceted cognitive structure for learning path recommendation, we propose a Cognitive Structure Enhanced framework for Adaptive Learning, named CSEAL. By viewing path recommendation as a Markov Decision Process and applying an actor-critic algorithm, CSEAL can sequentially identify the right learning items to different learners. Specifically, we first utilize a recurrent neural network to trace the evolving knowledge levels of learners at each learning step. Then, we design a navigation algorithm on the knowledge structure to ensure the logicality of learning paths, which reduces the search space in the decision process. Finally, the actor-critic algorithm is used to determine what to learn next and whose parameters are dynamically updated along the learning path. Extensive experiments on real-world data demonstrate the effectiveness and robustness of CSEAL.

student modeling
adaptive learning recommendation


E. Comparison With State-Of-The-Art Models

The first five columns are ODIs from the Salient360! dataset, and the last three columns are ODIs from the Saliency in VR dataset #REFR .

At each prediction stage, the dilated convolutional neural network was used to acquire a larger receptive field, and the SE blocks were used to assign different alpha-values to different layers according to their channel attentions, to help make more precise saliency prediction.

Besides, we shared weights among all the layers to make our model lightweight.

Through all the aforementioned operations, the proposed MRGAN360 model is both more accurate and lightweight than the other state-of-the-art methods.

In our future work, we plan to improve the model on detecting finer yet more accurate features in high-resolution 360 o images.

The implementation codes and pre-trained models will be made publicly available. dataset #OTHEREFR and the Saliency in VR dataset.

Mrgan360: Multi-Stage Recurrent Generative Adversarial Network For 360 Degree Image Saliency Prediction

Abstract: Thanks to the ability of providing an immersive and interactive experience, the uptake of 360 degree image content has been rapidly growing in consumer and industrial applications. Compared to planar 2D images, saliency prediction for 360 degree images is more challenging due to their high resolutions and spherical viewing ranges. Currently, most high-performance saliency prediction models for omnidirectional images (ODIs) rely on deeper or broader convolutional neural networks (CNNs), which benefit from CNNs' superior feature representation capabilities while suffering from their high computational costs. In this paper, inspired by the human visual cognitive process, i.e., human being's perception of a visual scene is always accomplished by multiple stages of analysis, we propose a novel multi-stage recurrent generative adversarial networks for ODIs dubbed MRGAN360, to predict the saliency maps stage by stage. At each stage, the prediction model takes as input the original image and the output of the previous stage and outputs a more accurate saliency map. We employ a recurrent neural network among adjacent prediction stages to model their correlations, and exploit a discriminator at the end of each stage to supervise the output saliency map. In addition, we share the weights among all the stages to obtain a lightweight architecture that is computationally cheap. Extensive experiments are conducted to demonstrate that our proposed model outperforms the stateof-the-art model in terms of both prediction accuracy and model size.

Saliency In Vr: How Do People Explore Virtual Environments?

Abstract: Understanding how people explore immersive virtual environments is crucial for many applications, such as designing virtual reality (VR) content, developing new compression algorithms, or learning computational models of saliency or visual attention. Whereas a body of recent work has focused on modeling saliency in desktop viewing conditions, VR is very different from these conditions in that viewing behavior is governed by stereoscopic vision and by the complex interaction of head orientation, gaze, and other kinematic constraints. To further our understanding of viewing behavior and saliency in VR, we capture and analyze gaze and head orientation data of 169 users exploring stereoscopic, static omni-directional panoramas, for a total of 1980 head and gaze trajectories for three different viewing conditions. We provide a thorough analysis of our data, which leads to several important insights, such as the existence of a particular fixation bias, which we then use to adapt existing saliency predictors to immersive VR conditions. In addition, we explore other applications of our data and analysis, including automatic alignment of VR video cuts, panorama thumbnails, panorama video synopsis, and saliency-basedcompression.


Dynamic Sparse Graph (DSG) #REFR activates a small number of critical neurons with the constructed sparse graph at every iteration dynamically.

CHannel EXploration (CHEX) #OTHEREFR uses two processes to dynamically adjust filter importance.

The first process is the channel pruning process with Column Subset Selection criterion #OTHEREFR .

The second process is the regrowing process, which is based on orthogonal projection #OTHEREFR to avoid regrowing redundant channels and to explore channel diversity.

Regrowing channels are restored to the most recently used (MRU) parameters rather than zeros.

To better preserve model accuracy, the remaining channels are re-distributed dynamically among all the layers.

DSG is developed from the argument that direct computation according to output activations is very costly for finding critical neurons.

The dimension reduction search is formulated to forecast the activation output by computing input and filters in a lower dimension.

Furthermore, to prevent BN layers from damaging sparsity, Liu et al.

introduce the double-mask selection that uses the same selection mask before and after BN layers #OTHEREFR .

Other methods such as SCP #OTHEREFR and DMC #OTHEREFR also maintain the mask dynamically. These are discussed in Section 2.3.

Structured Pruning For Deep Convolutional Neural Networks: A Survey

Abstract: The remarkable performance of deep Convolutional neural networks (CNNs) is generally attributed to their deeper and wider architectures, which can come with significant computational costs. Pruning neural networks has thus gained interest since it effectively lowers storage and computational costs. In contrast to weight pruning, which results in unstructured models, structured pruning provides the benefit of realistic acceleration by producing models that are friendly to hardware implementation. The special requirements of structured pruning have led to the discovery of numerous new challenges and the development of innovative solutions. This article surveys the recent progress towards structured pruning of deep CNNs. We summarize and compare the state-of-the-art structured pruning techniques with respect to filter ranking methods, regularization methods, dynamic execution, neural architecture search, the lottery ticket hypothesis, and the applications of pruning. While discussing structured pruning algorithms, we briefly introduce the unstructured pruning counterpart to emphasize their differences. Furthermore, we provide insights into potential research opportunities in the field of structured pruning. A curated list of neural network pruning papers can be found at https://github.com/he-y/Awesome-Pruning.

Dynamic Sparse Graph For Efficient Deep Learning

Abstract: We propose to execute deep neural networks (DNNs) with dynamic and sparse graph (DSG) structure for compressive memory and accelerative execution during both training and inference. The great success of DNNs motivates the pursuing of lightweight models for the deployment onto embedded devices. However, most of the previous studies optimize for inference while neglect training or even complicate it. Training is far more intractable, since (i) the neurons dominate the memory cost rather than the weights in inference; (ii) the dynamic activation makes previous sparse acceleration via one-off optimization on fixed weight invalid; (iii) batch normalization (BN) is critical for maintaining accuracy while its activation reorganization damages the sparsity. To address these issues, DSG activates only a small amount of neurons with high selectivity at each iteration via a dimension-reduction search (DRS) and obtains the BN compatibility via a double-mask selection (DMS). Experiments show significant memory saving (1.7-4.5x) and operation reduction (2.3-4.4x) with little accuracy loss on various benchmarks.


All MBConv blocks map to d m → 2 × d m → d m channels and contain Squeeze-Excitation layers #REFR .

They combine depthwhise convolution and regular pointwise convolutions for computationally efficient spatial encoding.

We perform temporal encoding on downsampled feature maps via the attention-based L-TAE #OTHEREFR , which is designed for satellite image time series and computationally more efficient than transformers. The network architecture is illustrated in Fig. 2 and further described in the following paragraphs.

Pre-aggregation shared encoder The T different input images are processed in parallel by a shared spatial encoding branch.

This encoder is composed of a pointwise convolution C in → d m , followed by a specifiable number n e of MBConv blocks.

Following #OTHEREFR we use group normal-isation in the encoding branch.

Ultimately, each input image X t is mapped to a feature map f t of the same resolution.

Attention-based temporal aggregation Following recent literature, we employ self-attention to aggregate a sequence of feature maps

We first down-sample features f t with a single maxpooling operation to low resolution feature maps f t of size d m × H × W .

Uncrtaints: Uncertainty Quantification For Cloud Removal In Optical Satellite Time Series

Abstract: Clouds and haze often occlude optical satellite images, hindering continuous, dense monitoring of the Earth's surface. Although modern deep learning methods can implicitly learn to ignore such occlusions, explicit cloud removal as pre-processing enables manual interpretation and allows training models when only few annotations are available. Cloud removal is challenging due to the wide range of occlusion scenarios-from scenes partially visible through haze, to completely opaque cloud coverage. Furthermore, integrating reconstructed images in downstream applications would greatly benefit from trustworthy quality assessment. In this paper, we introduce UnCRtainTS, a method for multi-temporal cloud removal combining a novel attentionbased architecture, and a formulation for multivariate uncertainty prediction. These two components combined set a new state-of-the-art performance in terms of image reconstruction on two public cloud removal datasets. Additionally, we show how the well-calibrated predicted uncertainties enable a precise control of the reconstruction quality.

Squeeze-And-Excitation Networks

Abstract: Convolutional neural networks are built upon the convolution operation, which extracts informative features by fusing spatial and channel-wise information together within local receptive fields. In order to boost the representational power of a network, much existing work has shown the benefits of enhancing spatial encoding. In this work, we focus on channels and propose a novel architectural unit, which we term the"Squeeze-and-Excitation"(SE) block, that adaptively recalibrates channel-wise feature responses by explicitly modelling interdependencies between channels. We demonstrate that by stacking these blocks together, we can construct SENet architectures that generalise extremely well across challenging datasets. Crucially, we find that SE blocks produce significant performance improvements for existing state-of-the-art deep architectures at slight computational cost. SENets formed the foundation of our ILSVRC 2017 classification submission which won first place and significantly reduced the top-5 error to 2.251%, achieving a 25% relative improvement over the winning entry of 2016.


The images are from the COCO dataset #REFR with a size of 640×480. We report results on its validation split.

Dataset We evaluated our method on the VQA-v2 dataset #OTHEREFR .

VQA-v2 covers 82.8k images and 443.8k questions for training and 40.5k images, and 214.4k questions for validation.

Model architecture and hyperparameters We mounted the attack on a commonly used visual question answering model called Modular Co-Attention Network (MCAN) #OTHEREFR that leverages several Modular Co-Attention (MCA) layers cascaded in depth.

Each MCA layer employs both the selfattention and guided-attention of input channels.

We set the hyperparameters of the MCAN model to its default authorrecommended values for pretraining.

We mounted the attack after training the model for 20 epochs showing no further improvement in its validation accuracy.

To mount the attack, we employed a batch size of 128, a learning rate of 0.0001 using Adam #OTHEREFR , and an epoch of two.

Instance-Level Trojan Attacks On Visual Question Answering Via Adversarial Learning In Neuron Activation Space

Abstract: Malicious perturbations embedded in input data, known as Trojan attacks, can cause neural networks to misbehave. However, the impact of a Trojan attack is reduced during fine-tuning of the model, which involves transferring knowledge from a pretrained large-scale model like visual question answering (VQA) to the target model. To mitigate the effects of a Trojan attack, replacing and fine-tuning multiple layers of the pretrained model is possible. This research focuses on sample efficiency, stealthiness and variation, and robustness to model fine-tuning. To address these challenges, we propose an instance-level Trojan attack that generates diverse Trojans across input samples and modalities. Adversarial learning establishes a correlation between a specified perturbation layer and the misbehavior of the fine-tuned model. We conducted extensive experiments on the VQA-v2 dataset using a range of metrics. The results show that our proposed method can effectively adapt to a fine-tuned model with minimal samples. Specifically, we found that a model with a single fine-tuning layer can be compromised using a single shot of adversarial samples, while a model with more fine-tuning layers can be compromised using only a few shots.

Microsoft Coco: Common Objects In Context

Abstract: We present a new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding. This is achieved by gathering images of complex everyday scenes containing common objects in their natural context. Objects are labeled using per-instance segmentations to aid in precise object localization. Our dataset contains photos of 91 objects types that would be easily recognizable by a 4 year old. With a total of 2.5 million labeled instances in 328k images, the creation of our dataset drew upon extensive crowd worker involvement via novel user interfaces for category detection, instance spotting and instance segmentation. We present a detailed statistical analysis of the dataset in comparison to PASCAL, ImageNet, and SUN. Finally, we provide baseline performance analysis for bounding box and segmentation detection results using a Deformable Parts Model.


The network is trained with RAdam #REFR with the initial learning rate as 1 × 10 −4 and a polynomial learning rate decay.

Each CT scan is resampled into the median spacing per tumor dataset (e.g., 3×0.419×0.419 mm for the pancreatic dataset) and normalized into zero mean and unit variance.

Our model is trained using a batch size of 2 on one GPU (with 28 × 192 × 320 patch size for pancreatic, 40 × 192 × 224 for liver).

We adopt the drop path #OTHEREFR strategy with a probability of 0.2 for regularization.

During training, extensive data augmentation is utilized on-the-fly #OTHEREFR to improve the generalization, including random rotation and scaling, elastic deformation, additive brightness, and gamma scaling.

We first pre-train the nnUNet backbone for 1000 epochs and finetune the whole architecture jointly for another 200 epochs.

During finetuning, we keep the backbone weights fixed for the first 50 epochs, and then set it with a learning rate multiplier of 0.1 for the next 150 epochs.

The number of object queries (i.e., cluster centers) N is 32, and the query distri-

Devil Is In The Queries: Advancing Mask Transformers For Real-World Medical Image Segmentation And Out-Of-Distribution Localization

Abstract: Real-world medical image segmentation has tremendous long-tailed complexity of objects, among which tail conditions correlate with relatively rare diseases and are clinically significant. A trustworthy medical AI algorithm should demonstrate its effectiveness on tail conditions to avoid clinically dangerous damage in these out-ofdistribution (OOD) cases. In this paper, we adopt the concept of object queries in Mask Transformers to formulate semantic segmentation as a soft cluster assignment. The queries fit the feature-level cluster centers of inliers during training. Therefore, when performing inference on a medical image in real-world scenarios, the similarity between pixels and the queries detects and localizes OOD regions. We term this OOD localization as MaxQuery. Furthermore, the foregrounds of real-world medical images, whether OOD objects or inliers, are lesions. The difference between them is less than that between the foreground and background, possibly misleading the object queries to focus redundantly on the background. Thus, we propose a query-distribution (QD) loss to enforce clear boundaries between segmentation targets and other regions at the query level, improving the inlier segmentation and OOD indication. Our proposed framework is tested on two realworld segmentation tasks, i.e., segmentation of pancreatic and liver tumors, outperforming previous state-of-the-art algorithms by an average of 7.39% on AUROC, 14.69% on AUPR, and 13.79% on FPR95 for OOD localization. On the other hand, our framework improves the performance of inlier segmentation by an average of 5.27% DSC when compared with the leading baseline nnUNet.

On The Variance Of The Adaptive Learning Rate And Beyond

Abstract: The learning rate warmup heuristic achieves remarkable success in stabilizing training, accelerating convergence and improving generalization for adaptive stochastic optimization algorithms like RMSprop and Adam. Pursuing the theory behind warmup, we identify a problem of the adaptive learning rate -- its variance is problematically large in the early stage, and presume warmup works as a variance reduction technique. We provide both empirical and theoretical evidence to verify our hypothesis. We further propose Rectified Adam (RAdam), a novel variant of Adam, by introducing a term to rectify the variance of the adaptive learning rate. Experimental results on image classification, language modeling, and neural machine translation verify our intuition and demonstrate the efficacy and robustness of RAdam.