Table 3 presents the results obtained by the different machine and deep learning algorithms on the LIAR dataset #REFR .

For our experiments, we used the dataset as it was initially released, with 6 labels #OTHEREFR (Table 3) , and by balancing the dataset's labels (Table 4) as proposed in #OTHEREFR .

To balance the labels, we created binary labels, i.e., all the texts that are not labeled with true are considered false.

Using the same experimental configurations as presented in Section 4.2, we obtained results that are aligned with our original observations on the proposed dataset.

Further, we obtained results similar to state-of-the-art results for the multi-label dataset, e.g., Wang #OTHEREFR and Alhindi et al. #OTHEREFR obtained an accuracy of ∼20%.

For the binary classification, we obtained results that go beyond the the state of the art, e.g.,Upadhayay and Behzadan #OTHEREFR obtains an accuracy of 70% while we obtain an accuracy of 83.99% with the LSTM model that employs the document embeddings constructed with GLOVE.

The dataset contains approximately 12.8K human annotated short statements collected using POLITIFACT.COM's API.

In this set of experiments, we used all the 6 labels of LIAR, i.e., pants-fire, false, barely-true, half-true, mostlytrue, and true, to build our classification models.

The dataset is highly imbalanced, as there are more news articles labeled with true than news articles labeled with the other five classes combined.

Due to this high degree of imbalance, the models performed poorly.

We observe that the best-performing models employ document embedding constructed with BART.

It'S All In The Embedding! Fake News Detection Using Document Embeddings

Abstract: With the current shift in the mass media landscape from journalistic rigor to social media, personalized social media is becoming the new norm. Although the digitalization progress of the media brings many advantages, it also increases the risk of spreading disinformation, misinformation, and malformation through the use of fake news. The emergence of this harmful phenomenon has managed to polarize society and manipulate public opinion on particular topics, e.g., elections, vaccinations, etc. Such information propagated on social media can distort public perceptions and generate social unrest while lacking the rigor of traditional journalism. Natural Language Processing and Machine Learning techniques are essential for developing efficient tools that can detect fake news. Models that use the context of textual data are essential for resolving the fake news detection problem, as they manage to encode linguistic features within the vector representation of words. In this paper, we propose a new approach that uses document embeddings to build multiple models that accurately label news articles as reliable or fake. We also present a benchmark on different architectures that detect fake news using binary or multi-labeled classification. We evaluated the models on five large news corpora using accuracy, precision, and recall. We obtained better results than more complex state-of-the-art Deep Neural Network models. We observe that the most important factor for obtaining high accuracy is the document encoding, not the classification model's complexity.

"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.


However, the model performance degradation is still significant compared with FL methods without considering privacy, such as FedAvg #REFR .

adopted for ensuring strong client-level DP.

However, this method includes two operations: clipping the l 2 norm of local updates to a sensitivity threshold C and adding random noise proportional to the model size, whose standard deviation (STD) is also decided by C.

These steps may cause severe performance degradation #OTHEREFR , #OTHEREFR , especially on large-scale complex model #OTHEREFR , such as ResNet-18 #OTHEREFR , or with heterogeneous data.

The reasons behind this issue are two-fold: (i) The useful information is dropped due to the clipping operation, especially with small C values, which is contained in the local updates; (ii) The model inconsistency among local models is exacerbated as the addition of random noise severely damages local updates and leads to large variances between local models, especially with large C values #OTHEREFR .

Existing works try to overcome these issues via restricting the norm of local update #OTHEREFR and leveraging local update sparsification technique #OTHEREFR , #OTHEREFR to reduce the adverse impacts of clipping and adding random noise.

Towards The Flatter Landscape And Better Generalization In Federated Learning Under Client-Level Differential Privacy

Abstract: To defend the inference attacks and mitigate the sensitive information leakages in Federated Learning (FL), client-level Differentially Private FL (DPFL) is the de-facto standard for privacy protection by clipping local updates and adding random noise. However, existing DPFL methods tend to make a sharp loss landscape and have poor weight perturbation robustness, resulting in severe performance degradation. To alleviate these issues, we propose a novel DPFL algorithm named DP-FedSAM, which leverages gradient perturbation to mitigate the negative impact of DP. Specifically, DP-FedSAM integrates Sharpness Aware Minimization (SAM) optimizer to generate local flatness models with improved stability and weight perturbation robustness, which results in the small norm of local updates and robustness to DP noise, thereby improving the performance. To further reduce the magnitude of random noise while achieving better performance, we propose DP-FedSAM-top k by adopting the local update sparsification technique. From the theoretical perspective, we present the convergence analysis to investigate how our algorithms mitigate the performance degradation induced by DP. Meanwhile, we give rigorous privacy guarantees with Rényi DP, the sensitivity analysis of local updates, and generalization analysis. At last, we empirically confirm that our algorithms achieve state-of-the-art (SOTA) performance compared with existing SOTA baselines in DPFL.

Communication-Efficient Learning Of Deep Networks From Decentralized Data

Abstract: Modern mobile devices have access to a wealth of data suitable for learning models, which in turn can greatly improve the user experience on the device. For example, language models can improve speech recognition and text entry, and image models can automatically select good photos. However, this rich data is often privacy sensitive, large in quantity, or both, which may preclude logging to the data center and training there using conventional approaches. We advocate an alternative that leaves the training data distributed on the mobile devices, and learns a shared model by aggregating locally-computed updates. We term this decentralized approach Federated Learning. We present a practical method for the federated learning of deep networks based on iterative model averaging, and conduct an extensive empirical evaluation, considering five different model architectures and four datasets. These experiments demonstrate the approach is robust to the unbalanced and non-IID data distributions that are a defining characteristic of this setting. Communication costs are the principal constraint, and we show a reduction in required communication rounds by 10-100x as compared to synchronized stochastic gradient descent.


Additionally, a drop path #REFR rate of 0.1 is employed to prevent overfitting, which randomly drops entire paths (i.e., sequences of layers) in the model during training.

While during testing, we only resize images to 224 × 224.

For our detector, we use a ResNet-50 #OTHEREFR pretrained on Ima-geNet #OTHEREFR .

The model is trained using the AdamW optimizer #OTHEREFR .

The learning rate was equal to 10 −3 , and a step scheduler with 5 epochs was used.

Weight decay is also applied with a factor of 5 • 10 −5 .

All training and evaluation processes were carried out on a server with one NVIDIA GeForce RTX 3060 GPU.

Improving Synthetically Generated Image Detection In Cross-Concept Settings

Abstract: New advancements for the detection of synthetic images are critical for fighting disinformation, as the capabilities of generative AI models continuously evolve and can lead to hyper-realistic synthetic imagery at unprecedented scale and speed. In this paper, we focus on the challenge of generalizing across different concept classes, e.g., when training a detector on human faces and testing on synthetic animal images-highlighting the ineffectiveness of existing approaches that randomly sample generated images to train their models. By contrast, we propose an approach based on the premise that the robustness of the detector can be enhanced by training it on realistic synthetic images that are selected based on their quality scores according to a probabilistic quality estimation model. We demonstrate the effectiveness of the proposed approach by conducting experiments with generated images from two seminal architectures, StyleGAN2 and Latent Diffusion, and using three different concepts for each, so as to measure the cross-concept generalization ability. Our results show that our quality-based sampling method leads to higher detection performance for nearly all concepts, improving the overall effectiveness of the synthetic image detectors.

Deep Networks With Stochastic Depth

Abstract: Very deep convolutional networks with hundreds of layers have led to significant reductions in error on competitive benchmarks. Although the unmatched expressiveness of the many layers can be highly desirable at test time, training very deep networks comes with its own set of challenges. The gradients can vanish, the forward flow often diminishes, and the training time can be painfully slow. To address these problems, we propose stochastic depth, a training procedure that enables the seemingly contradictory setup to train short networks and use deep networks at test time. We start with very deep networks but during training, for each mini-batch, randomly drop a subset of layers and bypass them with the identity function. This simple approach complements the recent success of residual networks. It reduces training time substantially and improves the test error significantly on almost all data sets that we used for evaluation. With stochastic depth we can increase the depth of residual networks even beyond 1200 layers and still yield meaningful improvements in test error (4.91 % on CIFAR-10).


To encode the 2D topology in R 2 into a prior distribution, we adopt the junction tree encoder architecture from JT-VAE #REFR .

The whole procedure is illustrated in Figure 3 and detailed next.

Graphvf: Controllable Protein-Specific 3D Molecule Generation With Variational Flow Fang Sun

Abstract: Designing molecules that bind to specific target proteins is a fundamental task in drug discovery. Recent models leverage geometric constraints to generate ligand molecules that bind cohesively with specific protein pockets. However, these models cannot effectively generate 3D molecules with 2D skeletal curtailments and property constraints, which are pivotal to drug potency and development. To tackle this challenge, we propose GraphVF, a variational flow-based framework that combines 2D topology and 3D geometry, for controllable generation of binding 3D molecules. Empirically, our method achieves state-of-the-art binding affinity and realistic sub-structural layouts for protein-specific generation. In particular, GraphVF represents the first controllable geometry-aware, protein-specific molecule generation method, which can generate binding 3D molecules with tailored sub-structures and physio-chemical properties. Our code is available at https://github.com/Franco-Solis/GraphVF-code.

Junction Tree Variational Autoencoder For Molecular Graph Generation

Abstract: We seek to automate the design of molecules based on specific chemical properties. In computational terms, this task involves continuous embedding and generation of molecular graphs. Our primary contribution is the direct realization of molecular graphs, a task previously approached by generating linear SMILES strings instead of graphs. Our junction tree variational autoencoder generates molecular graphs in two phases, by first generating a tree-structured scaffold over chemical substructures, and then combining them into a molecule with a graph message passing network. This approach allows us to incrementally expand molecules while maintaining chemical validity at every step. We evaluate our model on multiple tasks ranging from molecular generation to optimization. Across these tasks, our model outperforms previous state-of-the-art baselines by a significant margin.


By comparing the results with one of the most comprehensive papers on component-based algorithms #REFR , we see that our group-based DiCA returns more accurate solutions in much smaller number of iterations.

For Algorithm 2, we need to set four parameters ρ v , ρ θ , ρ p and ρ q .

For simplicity and based on numerical results, we control all of them with a single scalar ρ as

Table III shows the results of running the DiCA algorithm for some problem instances.

The values for ρ are chosen as the one that yields the minimum number of iterations among some multiples of 50.

Some of the common cases reported in #OTHEREFR are given in Table IV for comparison.

Figure 3 shows the plots of the minimum residual of the regions versus the number of iterations for different values of ρ.

As can be seen, ρ can significantly change the number of iterations.

As also reported in #OTHEREFR , the oscillations in the progress of the residual slow down the convergence of the distributed algorithms. Figure 4 shows the number of iterations versus ρ.

For all the tested problems, DiCA does not converge if ρ is smaller than a certain limit, for example, in Figure 3 for the case300 problem, when ρ = 200.

Distributed Optimization For Power Systems With Radial Partitioning

Abstract: This paper proposes group-based distributed optimization algorithms on top of intelligent partitioning for the optimal power flow (OPF) problem. Radial partitioning of the graph of a network is introduced as a systematic way to split a large-scale problem into more tractable sub-problems, which can potentially be solved efficiently with methods such as convex relaxations. The simple implementation of a Distributed Consensus Algorithm (DiCA) with very few parameters makes it viable for different parameter selection methods, which are crucial for the fast convergence of the distributed algorithms. The DiCA algorithm returns more accurate solutions to the tested problems with fewer iterations than component-based algorithms. Our numerical results show the performance of the algorithms for different power network instances and the effect of parameter selection. A software package DiCARP is created, which is implemented in Python using the Pyomo optimization package.

A Component-Based Dual Decomposition Method For The Opf Problem

Abstract: This paper proposes a component-based dual decomposition of the nonconvex AC optimal power flow (OPF) problem, where the modified dual function is solved in a distributed fashion. The main contribution of this work is that is demonstrates that a distributed method with carefully tuned parameters can converge to globally optimal solutions despite the inherent nonconvexity of the problem and the absence of theoretical guarantees of convergence. This paper is the first to conduct extensive numerical analysis resulting in the identification and tabulation of the algorithmic parameter settings that are crucial for the convergence of the method on 72 AC OPF test instances. Moreover, this work provides a deeper insight into the geometry of the modified Lagrange dual function of the OPF problem and highlights the conditions that make this function differentiable. This numerical demonstration of convergence coupled with the scalability and the privacy preserving nature of the proposed method makes it well suited for smart grid applications such as multi-period OPF with demand response (DR) and security constrained unit commitment (SCUC) with contingency constraints and multiple transmission system operators (TSOs).

group-based DiCA
component-based algorithms


Among the different prediction tasks in the Predictive Process Monitoring state-of-the-art, an important group of papers focuses, as this work, on predicting outcomes #REFR .

To the best of our knowledge, no other works exist on the accuracy improvement exploiting frequent explanations, except for our previous work #OTHEREFR .

We hence first position our work with respect to the works in Predictive Process Monitoring, the works focused on explanations in Predictive Process Monitoring, and the works improving the predictive model's performance employing (user) feedback; we finally address the specific comparison with our previous work #OTHEREFR .

For instance, several approaches deal with predicting the fulfilment (or the violation) of a boolean predicate in a running case #OTHEREFR , #OTHEREFR .

Initial approaches and encodings for outcome-oriented Predictive Process Monitoring have been enhanced in terms of performance by introducing pre-processing steps based on clustering and bucketing #OTHEREFR , #OTHEREFR , as well as in terms of prediction accuracy by introducing different types of encodings #OTHEREFR , #OTHEREFR .

Moreover, more recently, deep learning approaches have also been investigated for predicting outcomes #OTHEREFR , #OTHEREFR .

Differently from these works, the focus of this paper is not on proposing a specific outcome prediction method, but rather on explaining why predictive models are wrong and on leveraging these explanations for eventually improving the performance of the predictive model.

A number of works have recently focused on providing explanations in Predictive Process Monitoring #OTHEREFR .

Explain, Adapt And Retrain: How To Improve The Accuracy Of A Ppm Classifier Through Different Explanation Styles

Abstract: Recent papers have introduced a novel approach to explain why a Predictive Process Monitoring (PPM) model for outcome-oriented predictions provides wrong predictions. Moreover, they have shown how to exploit the explanations, obtained using state-of-the art post-hoc explainers, to identify the most common features that induce a predictor to make mistakes in a semi-automated way, and, in turn, to reduce the impact of those features and increase the accuracy of the predictive model. This work starts from the assumption that frequent control flow patterns in event logs may represent important features that characterize, and therefore explain, a certain prediction. Therefore, in this paper, we (i) employ a novel encoding able to leverage DECLARE constraints in Predictive Process Monitoring and compare the effectiveness of this encoding with Predictive Process Monitoring state-of-the art encodings, in particular for the task of outcome-oriented predictions; (ii) introduce a completely automated pipeline for the identification of the most common features inducing a predictor to make mistakes; and (iii) show the effectiveness of the proposed pipeline in increasing the accuracy of the predictive model by validating it on different real-life datasets.

Outcome-Oriented Predictive Process Monitoring: Review And Benchmark

Abstract: Predictive business process monitoring refers to the act of making predictions about the future state of ongoing cases of a business process, based on their incomplete execution traces and logs of historical (completed) traces. Motivated by the increasingly pervasive availability of fine-grained event data about business process executions, the problem of predictive process monitoring has received substantial attention in the past years. In particular, a considerable number of methods have been put forward to address the problem of outcome-oriented predictive process monitoring, which refers to classifying each ongoing case of a process according to a given set of possible outcomes - e.g. Will the customer complain or not? Will an order be delivered, cancelled or withdrawn? Unfortunately, different authors have used different datasets, experimental settings, evaluation measures and baselines to assess their proposals, resulting in poor comparability and an unclear picture of the relative merits and applicability of different methods. To address this gap, this article presents a systematic review and taxonomy of outcome-oriented predictive process monitoring methods, and a comparative experimental evaluation of eleven representative methods using a benchmark covering twelve predictive process monitoring tasks based on four real-life event logs.


Next, a thresholding step is done, where the probabilities lower than a dynamic threshold τ are truncated to zero, while redistributing the remaining probabilities. For more details refer to the paper #REFR .

The key idea here is to turn these non-consequential pairwise connections to zero attention weights, while bumping the attention score of the important ones, i.e., a sparse attention distribution.

In particular, Sparsegen-lin activation projects the attention scores a ∈ R n onto a probability simplex p ∈ R n , along with a regularization coefficient λ < 1:

with, ∆ n−1 = {p ∈ R n | p = 1, p > 0} enforcing constraints of probabilities summing to one and greater than non-zero.

Note, the L2 norm with negative λ regularization acts to actually assign larger probability values in p, as the objective is to minimize the cost function above.

We can use this Sparse Self-Attention by replacing the softmax with Sparsegen function:

Note, sparse (self-)attention and its variants has been used in NLP works with impressive results #OTHEREFR as well as time series forecasting #OTHEREFR .

Filling Out The Missing Gaps: Time Series Imputation With Semi-Supervised Learning

Abstract: Missing data in time series is a challenging issue affecting time series analysis. Missing data occurs due to problems like data drops or sensor malfunctioning. Imputation methods are used to fill in these values, with quality of imputation having a significant impact on downstream tasks like classification. In this work, we propose a semi-supervised imputation method, ST-Impute, that uses both unlabeled data along with downstream task's labeled data. ST-Impute is based on sparse self-attention and trains on tasks that mimic the imputation process. Our results indicate that the proposed method outperforms the existing supervised and unsupervised time series imputation methods measured on the imputation quality as well as on the downstream tasks ingesting imputed time series.

On Controllable Sparse Alternatives To Softmax

Abstract: Converting an n-dimensional vector to a probability distribution over n objects is a commonly used component in many machine learning tasks like multiclass classification, multilabel classification, attention mechanisms etc. For this, several probability mapping functions have been proposed and employed in literature such as softmax, sum-normalization, spherical softmax, and sparsemax, but there is very little understanding in terms how they relate with each other. Further, none of the above formulations offer an explicit control over the degree of sparsity. To address this, we develop a unified framework that encompasses all these formulations as special cases. This framework ensures simple closed-form solutions and existence of sub-gradients suitable for learning via backpropagation. Within this framework, we propose two novel sparse formulations, sparsegen-lin and sparsehourglass, that seek to provide a control over the degree of desired sparsity. We further develop novel convex loss functions that help induce the behavior of aforementioned formulations in the multilabel classification setting, showing improved performance. We also demonstrate empirically that the proposed formulations, when used to compute attention weights, achieve better or comparable performance on standard seq2seq tasks like neural machine translation and abstractive summarization.


Note that (Chai et al., 2021) can only generate one deterministic output given the intput collage (first row), whereas #REFR generates images with little diversity (second row).

When training with random crops, the boxes are obtained by sampling from the ground-truth box distribution, which means that the bounding box distribution at inference time is in-distribution, what could explain the lower image FID when compared to Mask-RCNN or OLN.

However, the latter methods obtain a better object FID, which could be the result of the bounding boxes being more likely to contain actual objects than the random crops baseline.

Moreover, in Figure 17 , we show generated images using out-of-distribution object combinations in a collage and observe that training with Mask-RCNN and OLN bounding boxes results in better object generations than when training with random crops, as already seen quantitatively in Table 12 , as well as visually comparable image quality and diversity to the model trained with ground-truth bounding boxes.

Overall, these experiments showcase that M&Ms can be trained without ground-truth bounding boxes to generate reasonable scenes, see Figure 17 , at the expense of worse image and object FID metrics for the in-distribution setting, compared to the model trained with ground-truth boxes.

Sample obtained with (Chai et al., 2021) Collage Samples obtained with in-domain image editing #OTHEREFR Samples obtained with M&Ms (a) Qualitative comparison with image editing methods (Chai et al., 2021; #OTHEREFR that admit a collage as input.

In contrast, M&Ms offers diverse outputs given the same collage (third row).

Note that neither of the image editing methods supports moving nor resizing the collage elements.

Comparisons are made with collages that include the novel class cacti.

Note that (Chai et al., 2021 ) can only generate one deterministic output given the intput collage (first row), whereas #OTHEREFR generates images with little diversity (second row).

Controllable Image Generation Via Collage Representations

Abstract: Recent advances in conditional generative image models have enabled impressive results. On the one hand, text-based conditional models have achieved remarkable generation quality, by leveraging large-scale datasets of image-text pairs. To enable fine-grained controllability, however, text-based models require long prompts, whose details may be ignored by the model. On the other hand, layout-based conditional models have also witnessed significant advances. These models rely on bounding boxes or segmentation maps for precise spatial conditioning in combination with coarse semantic labels. The semantic labels, however, cannot be used to express detailed appearance characteristics. In this paper, we approach fine-grained scene controllability through image collages which allow a rich visual description of the desired scene as well as the appearance and location of the objects therein, without the need of class nor attribute labels. We introduce "mixing and matching scenes" (M&Ms), an approach that consists of an adversarially trained generative image model which is conditioned on appearance features and spatial positions of the different elements in a collage, and integrates these into a coherent image. We train our model on the OpenImages (OI) dataset and evaluate it on collages derived from OI and MS-COCO datasets. Our experiments on the OI dataset show that M&Ms outperforms baselines in terms of fine-grained scene controllability while being very competitive in terms of image quality and sample diversity. On the MS-COCO dataset, we highlight the generalization ability of our model by outperforming DALL-E in terms of the zero-shot FID metric, despite using two magnitudes fewer parameters and data. Collage based generative models have the potential to advance content creation in an efficient and effective way as they are intuitive to use and yield high quality generations.

In-Domain Gan Inversion For Real Image Editing

Abstract: Recent work has shown that a variety of controllable semantics emerges in the latent space of the Generative Adversarial Networks (GANs) when being trained to synthesize images. However, it is difficult to use these learned semantics for real image editing. A common practice of feeding a real image to a trained GAN generator is to invert it back to a latent code. However, we find that existing inversion methods typically focus on reconstructing the target image by pixel values yet fail to land the inverted code in the semantic domain of the original latent space. As a result, the reconstructed image cannot well support semantic editing through varying the latent code. To solve this problem, we propose an in-domain GAN inversion approach, which not only faithfully reconstructs the input image but also ensures the inverted code to be semantically meaningful for editing. We first learn a novel domain-guided encoder to project any given image to the native latent space of GANs. We then propose a domain-regularized optimization by involving the encoder as a regularizer to fine-tune the code produced by the encoder, which better recovers the target image. Extensive experiments suggest that our inversion method achieves satisfying real image reconstruction and more importantly facilitates various image editing tasks, such as image interpolation and semantic manipulation, significantly outperforming start-of-the-arts.


In this appendix, we detail the derivation of our first benchmarking SDP 1 , based on the SDP for fidelity #REFR . Let and be bipartite states.

The SDP for the root fidelity √ ( , ), which makes use of Uhlmann's theorem #OTHEREFR , is as follows:

where L (H ) is the set of all linear operators acting on the Hilbert space H .

Note that there is no semidefinite constraint that directly corresponds to optimizing over the set of separable states #OTHEREFR .

Instead, we can constrain to have a positive partial transpose (PPT) #OTHEREFR and be -extendible #OTHEREFR , since all separable states satisfy these constraints.

Quantum Steering Algorithm For Estimating Fidelity Of Separability

Abstract: Quantifying entanglement is an important task by which the resourcefulness of a state can be measured. Here we develop a quantum algorithm that tests for and quantifies the separability of a general bipartite state, by making use of the quantum steering effect. Our first separability test consists of a distributed quantum computation involving two parties: a computationally limited client, who prepares a purification of the state of interest, and a computationally unbounded server, who tries to steer the reduced systems to a probabilistic ensemble of pure product states. To design a practical algorithm, we replace the role of the server by a combination of parameterized unitary circuits and classical optimization techniques to perform the necessary computation. The result is a variational quantum steering algorithm (VQSA), which is our second separability test that is better suited for the capabilities of quantum computers available today. This VQSA has an additional interpretation as a distributed variational quantum algorithm (VQA) that can be executed over a quantum network, in which each node is equipped with classical and quantum computers capable of executing VQA. We then simulate our VQSA on noisy quantum simulators and find favorable convergence properties on the examples tested. We also develop semidefinite programs, executable on classical computers, that benchmark the results obtained from our VQSA. Our findings here thus provide a meaningful connection between steering, entanglement, quantum algorithms, and quantum computational complexity theory. They also demonstrate the value of a parameterized mid-circuit measurement in a VQSA and represent a first-of-its-kind application for a distributed VQA. Finally, the whole framework generalizes to the case of multipartite states and entanglement.

Simpler Semidefinite Programs For Completely Bounded Norms

Abstract: The completely bounded trace and spectral norms, for finite-dimensional spaces, are known to be efficiently expressible by semidefinite programs (J. Watrous, Theory of Computing 5: 11, 2009). This paper presents two new, and arguably much simpler, semidefinite programming formulations of these norms.


To solve for dynamics of mass flows and pressures across the system we use the staggered-grid approach of #REFR which is an explicit, conservative, second order, finite difference scheme, stable given a CFL condition is satisfied.

Where S ij is the cross-section of the pipe.

Initial conditions for density and mass-flux in the system are constructed based on actual operational data.

We remind that, as of now, the Israel system does not contain compressors.

Control Of Line Pack In Natural Gas System: Balancing Limited Resources Under Uncertainty

Abstract: We build and experiment with a realistic but reduced natural gas model of Israel. The system is unusual because (a) it is controlled from a limited number of points which are at, or close to, the gas extraction sites offshore of Israel's Mediterranean coast; (b) control specifies average flux at inlet, not pressure; (c) there are no inland compressors to regulate pressure; (d) power system is the main consumer of gas (70% of Israel's power is generated at gas-fired power plants distributed across the country). Nature of the system suggests that a special attention should be given to understanding dynamics driven by fast transients in gas consumption meeting intraday variations in the electricity demand, and accounting for increasing role of uncertain renewable generation (mainly solar). Based on all of the above we pose and resolve a sequence of dynamic and control challenges, such as: How to time ramping up-and down-injection of gas to guarantee a healthy intra-day line-pack which meets both pressure constraints and gas-extraction patterns? We report simulation results and utilize monotonicity properties of the natural gas flows which render robustness of our conclusions to the uncertainties of the edge withdrawals of gas.

An Explicit Staggered-Grid Method For Numerical Simulation Of Large-Scale Natural Gas Pipeline Networks

Abstract: We present an explicit second order staggered finite difference (FD) discretization scheme for forward simulation of natural gas transport in pipeline networks. By construction, this discretization approach guarantees that the conservation of mass condition is satisfied exactly. The mathematical model is formulated in terms of density, pressure, and mass flux variables, and as a result permits the use of a general equation of state to define the relation between the gas density and pressure for a given temperature. In a single pipe, the model represents the dynamics of the density by propagation of a non-linear wave according to a variable wave speed. We derive compatibility conditions for linking domain boundary values to enable efficient, explicit simulation of gas flows propagating through a network with pressure changes created by gas compressors. We compare our staggered grid method with an explicit operator splitting method and a lumped element scheme, and perform numerical experiments to validate the convergence order of the new discretization approach. In addition, we perform several computations to investigate the influence of non-ideal equation of state models and temperature effects on pipeline simulations with boundary conditions on various time and space scales.

staggered-grid approach
finite difference scheme


In order to force the training generator to produce more realistic outputs, we employ a global discriminator and three local discriminators against the generator. We use SN-PatchGAN #REFR as our global discriminator.

The generator adopts an encoder-decoder architecture with skip connections #OTHEREFR , as illustrated in Fig. 3(a) .

Specifically, the encoder is composed of several successive gated convolutional layers #OTHEREFR with stride 2.

The decoder is composed of several Half-AdaIN with upsampling operations.

In addition, two CWSI are inserted into the decoder at the resolution of 64 × 64 and 128 × 128.

For the inpainting task, we generally believe that the encoder can transfer the available information from the known pixels to missing pixels by gradually increasing the receptive field, and the decoder is responsible for the reconstruction of details #OTHEREFR , so we only place the control modules in the decoder.

The local discriminators are focused on specific sub-regions, including two eyes and mouths, which helps the generator synthesize high-frequency textures in these regions. The discriminators are omitted in Fig. 3 .

Reference-Guided Large-Scale Face Inpainting With Identity And Texture Control

Abstract: Face inpainting aims at plausibly predicting missing pixels of face images within a corrupted region. Most existing methods rely on generative models learning a face image distribution from a big dataset, which produces uncontrollable results, especially with large-scale missing regions. To introduce strong control for face inpainting, we propose a novel reference-guided face inpainting method that fills the large-scale missing region with identity and texture control guided by a reference face image. However, generating high-quality results under imposing two control signals is challenging. To tackle such difficulty, we propose a dual control one-stage framework that decouples the reference image into two levels for flexible control: High-level identity information and low-level texture information, where the identity information figures out the shape of the face and the texture information depicts the component-aware texture. To synthesize high-quality results, we design two novel modules referred to as Half-AdaIN and Component-Wise Style Injector (CWSI) to inject the two kinds of control information into the inpainting processing. Our method produces realistic results with identity and texture control faithful to reference images. To the best of our knowledge, it is the first work to concurrently apply identity and component-level controls in face inpainting to promise more precise and controllable results.

Free-Form Image Inpainting With Gated Convolution

Abstract: We present a generative image inpainting system to complete images with free-form mask and guidance. The system is based on gated convolutions learned from millions of images without additional labelling efforts. The proposed gated convolution solves the issue of vanilla convolution that treats all input pixels as valid ones, generalizes partial convolution by providing a learnable dynamic feature selection mechanism for each channel at each spatial location across all layers. Moreover, as free-form masks may appear anywhere in images with any shape, global and local GANs designed for a single rectangular mask are not applicable. Thus, we also present a patch-based GAN loss, named SN-PatchGAN, by applying spectral-normalized discriminator on dense image patches. SN-PatchGAN is simple in formulation, fast and stable in training. Results on automatic image inpainting and user-guided extension demonstrate that our system generates higher-quality and more flexible results than previous methods. Our system helps user quickly remove distracting objects, modify image layouts, clear watermarks and edit faces. Code, demo and models are available at: \url{https://github.com/JiahuiYu/generative_inpainting}.


The main idea is learning a reward function to reflect human preferences with human annotations and optimize language models by RL methods like proximal policy optimization (PPO) #REFR .

Alignment #OTHEREFR is first proposed to build agents that behave in accordance with the human's intention.

By communicating with human, agents can get accurate supervised signals #OTHEREFR by applying several scalable reward learning methods #OTHEREFR 20] .

Alignment benefits many recent generative foundation models, like InstructGPT #OTHEREFR , Claude #OTHEREFR and Sparrow #OTHEREFR , in achieving better performance.

In language foundation model training #OTHEREFR , alignment is often achieved by Reinforcement Learning from Human Feedback (RLHF).

By incorporating supervised finetuning (SFT), InstructGPT #OTHEREFR successfully achieved alignment for GPT-3 #OTHEREFR .

Besides, Claude #OTHEREFR and Sparrow #OTHEREFR stressed aligning language foundation models from helpful, honest, and harmless(HHH) human feedbacks.

In visual generative models, several works #OTHEREFR studied aligning them with human feedbacks.

Models are expected to understand specific visual control signals like colors, counts, and backgrounds #OTHEREFR more accurately after alignment.

It is still challenging to achieve tradeoffs between aligning human preferences and generating high-fidelity images.

Raft: Reward Ranked Finetuning For Generative Foundation Model Alignment

Abstract: Generative foundation models are susceptible to implicit biases that can arise from extensive unsupervised training data. Such biases can produce suboptimal samples, skewed outcomes, and unfairness, with potentially significant repercussions. Consequently, aligning these models with human ethics and preferences is an essential step toward ensuring their responsible and effective deployment in real-world applications. Prior research has primarily employed Reinforcement Learning from Human Feedback (RLHF) as a means of addressing this problem, wherein generative models are fine-tuned using RL algorithms guided by a humanfeedback-informed reward model. However, the inefficiencies and instabilities associated with RL algorithms frequently present substantial obstacles to the successful alignment of generative models, necessitating the development of a more robust and streamlined approach. To this end, we introduce a new framework, Reward rAnked FineTuning (RAFT), designed to align generative models more effectively. Utilizing a reward model and a sufficient number of samples, our approach selects the high-quality samples, discarding those that exhibit undesired behavior, and subsequently assembles a streaming dataset. This dataset serves as the basis for aligning the generative model and can be employed under both offline and online settings. Notably, the sample generation process within RAFT is gradient-free, rendering it compatible with black-box generators. Through extensive experiments, we demonstrate that our proposed algorithm exhibits strong performance in the context of both large language models and diffusion models.

Proximal Policy Optimization Algorithms

Abstract: We propose a new family of policy gradient methods for reinforcement learning, which alternate between sampling data through interaction with the environment, and optimizing a "surrogate" objective function using stochastic gradient ascent. Whereas standard policy gradient methods perform one gradient update per data sample, we propose a novel objective function that enables multiple epochs of minibatch updates. The new methods, which we call proximal policy optimization (PPO), have some of the benefits of trust region policy optimization (TRPO), but they are much simpler to implement, more general, and have better sample complexity (empirically). Our experiments test PPO on a collection of benchmark tasks, including simulated robotic locomotion and Atari game playing, and we show that PPO outperforms other online policy gradient methods, and overall strikes a favorable balance between sample complexity, simplicity, and wall-time.


Counterfactual fairness #REFR examines how perturbing the demographic signals of existing test examples can change the performance of the model (e.g.

Current LLM evaluation mechanisms include quantitative metrics measuring notions of accuracy (how similar are the generated outputs to the expected outputs), robustness (how resilient is the model to transformations of the input), calibration (how meaningful are the generated probabilities in respect to uncertainty), efficiency (what are the energy, carbon, and time costs for training and inference) and more #OTHEREFR .

Some also go beyond singular trainingtime loss objectives and implement reinforcement learning using human feedback in the loop #OTHEREFR .

A variety of potential harms and failure modes for LLMs have also been identified.

There are substantial environmental costs associated with the volume of computational power required for training and inference #OTHEREFR .

There are also concerns of LLMs propagating unfairness or bias.

"He worked at the local hospital" versus "She worked at the local hospital").

Fairness can also be evaluated via performance disparities between demographic subgroups.

There are also other concerning forms of biases such as stereotypical associations, erasure, and over-representation in the semantics of its output #OTHEREFR . Finally, LLMs have been shown to produce toxic outputs.

Toxicity in this context refers to hateful, violent, or offensive text #OTHEREFR , and has been shown to result even when the text prompt input is not itself toxic #OTHEREFR .

LLMs often suffer from factual errors-they can "hallucinate" information #OTHEREFR by providing very confident-sounding but entirely false responses.

Who'S Thinking? A Push For Human-Centered Evaluation Of Llms Using The Xai Playbook

Abstract: Deployed artificial intelligence (AI) often impacts humans, and there is no one-size-fits-all metric to evaluate these tools. Human-centered evaluation of AI-based systems combines quantitative and qualitative analysis and human input. It has been explored to some depth in the explainable AI (XAI) and human-computer interaction (HCI) communities. Gaps remain, but the basic understanding that humans interact with AI and accompanying explanations, and that humans' needs-complete with their cognitive biases and quirksshould be held front and center, is accepted by the community. In this paper, we draw parallels between the relatively mature field of XAI and the rapidly evolving research boom around large language models (LLMs). Accepted evaluative metrics for LLMs are not human-centered. We argue that many of the same paths tread by the XAI community over the past decade will be retread when discussing LLMs. Specifically, we argue that humans' tendenciesagain, complete with their cognitive biases and quirks-should rest front and center when evaluating deployed LLMs. We outline three developed focus areas of human-centered evaluation of XAI: mental models, use case utility, and cognitive engagement, and we highlight the importance of exploring each of these concepts for LLMs. Our goal is to jumpstart human-centered LLM evaluation. CCS CONCEPTS • Human-centered computing → User models; HCI theory, concepts and models; • Computing methodologies → Philosophical/theoretical foundations of artificial intelligence; Artificial intelligence.

Reducing Sentiment Bias In Language Models Via Counterfactual Evaluation

Abstract: Recent improvements in large-scale language models have driven progress on automatic generation of syntactically and semantically consistent text for many real-world applications. Many of these advances leverage the availability of large corpora. While training on such corpora encourages the model to understand long-range dependencies in text, it can also result in the models internalizing the social biases present in the corpora. This paper aims to quantify and reduce biases exhibited by language models. Given a conditioning context (e.g. a writing prompt) and a language model, we analyze if (and how) the sentiment of the generated text is affected by changes in values of sensitive attributes (e.g. country names, occupations, genders, etc.) in the conditioning context, a.k.a. counterfactual evaluation. We quantify these biases by adapting individual and group fairness metrics from the fair machine learning literature. Extensive evaluation on two different corpora (news articles and Wikipedia) shows that state-of-the-art Transformer-based language models exhibit biases learned from data. We propose embedding-similarity and sentiment-similarity regularization methods that improve both individual and group fairness metrics without sacrificing perplexity and semantic similarity---a positive step toward development and deployment of fairer language models for real-world applications.


Following #REFR , we predict the 3D pose for the central frame from any such sequence, i.e.p i/2 ∈ R J×3 .

In the following subsections, we present an overview of our solution methodology for estimating 3D poses from a sequence of 2D poses, then we describe in our global network architecture, and lastly we present our dynamic multi-headed convolutional self-attention mechanism.

The overall architecture of our methodology is described in Figure 1 .

Given a sequence of 2D poses P = {Pi} T i=1 ⊂ R J×2 where T represents the number of frames in the sequence and J is the number of joints in the skeleton.

We seek to reconstruct the 3D poses in the root relative camera reference frame (i.e.

the camera reference frame where the root joint sits at the origin).

Our network contains two Dynamic ConvFormer blocks, one with spatial attention and the other with temporal attention.

More specifically, we leverage a spatial attention mechanism to extract frame-wise inter-joint dependencies by analyzing sections of joints that are related.

The temporal attention mechanism extracts global inter-frame relationships by analyzing correlations between the temporal profiles of joints.

In contrast to #OTHEREFR , which queries latent pose representations for individual frames and then computes attention with respect to the temporal axis, our temporal joints profile mechanism fuses temporal information at the querying level prior to computing self-attention with respect to the temporal axis.

Convformer: Parameter Reduction In Transformer Models For 3D Human Pose Estimation By Leveraging Dynamic Multi-Headed Convolutional Attention

Abstract: Recently, fully-transformer architectures have replaced the defacto convolutional architecture for the 3D human pose estimation task. In this paper we propose ConvFormer , a novel convolutional transformer that leverages a new dynamic multi-headed convolutional self-attention mechanism for monocular 3D human pose estimation. We designed a spatial and temporal convolutional transformer to comprehensively model human joint relations within individual frames and globally across the motion sequence. Moreover, we introduce a novel notion of temporal joints profile for our temporal ConvFormer that fuses complete temporal information immediately for a local neighborhood of joint features. We have quantitatively and qualitatively validated our method on three common benchmark datasets: Human3.6M, MPI-INF-3DHP, and HumanEva. Extensive experiments have been conducted to identify the optimal hyper-parameter set. These experiments demonstrated that we achieved a significant parameter reduction relative to prior transformer models while attaining State-of-the-Art (SOTA) or near SOTA on all three datasets. Additionally, we achieved SOTA for Protocol III on H36M for both GT and CPN detection inputs. Finally, we obtained SOTA on all three metrics for the MPI-INF-3DHP dataset and for all three subjects on HumanEva under Protocol II.

3D Human Pose Estimation In Video With Temporal Convolutions And Semi-Supervised Training

Abstract: In this work, we demonstrate that 3D poses in video can be effectively estimated with a fully convolutional model based on dilated temporal convolutions over 2D keypoints. We also introduce back-projection, a simple and effective semi-supervised training method that leverages unlabeled video data. We start with predicted 2D keypoints for unlabeled video, then estimate 3D poses and finally back-project to the input 2D keypoints. In the supervised setting, our fully-convolutional model outperforms the previous best result from the literature by 6 mm mean per-joint position error on Human3.6M, corresponding to an error reduction of 11%, and the model also shows significant improvements on HumanEva-I. Moreover, experiments with back-projection show that it comfortably outperforms previous state-of-the-art results in semi-supervised settings where labeled data is scarce. Code and models are available at https://github.com/facebookresearch/VideoPose3D


The first image of Figure 1 requires knowledge about human culture and history #REFR , to combine with visual information: the object in the image is a church, and people usually go to the church on Sundays.

For example, masking out words from image captions enforces learning how to fill them based on visual cues; reversely, image regions can be masked out, with language guiding their reconstruction.

Task-specific fine-tuning steps upon this basic understanding of vision and language, by refining the neural weights of the trained model to adapt to each specific task at a time, upon which the final evaluation is performed.

Despite the rich VL knowledge acquired during this process, current transformer-based VL models #OTHEREFR lack generalization to several concepts and scenarios that require commonsense knowledge, or knowledge of abstract entities, facts and real-world events.

Of course, this is somehow expected, since neither pre-training nor fine-tuning VL datasets contain or demand perceiving concepts beyond visual descriptions.

Figure 1 presents some examples of this claim: questions (Q) about the image (I ) require some knowledge beyond the visual domain, so that the correct answer (A) can be inferred. #OTHEREFR .

The second image #OTHEREFR requires one more reasoning step, since it is not only required to detect that this is a postage stamp containing the photo of a person (visual information), but also who this person is.

Knowledge about named entities recognizes this person as Alexander Hamilton.

Further factual knowledge provides that Alexander Hamilton was born in todays Saint Kitts and Nevis and Saint Kitts and Nevis is in North America.

The combination of these two facts derives the final answer Alexander Hamilton was born in North America.

The third image #OTHEREFR requires the visual extraction of the two people present in it.

The Contribution Of Knowledge In Visiolinguistic Learning: A Survey On Tasks And Challenges

Abstract: Recent advancements in visiolinguistic (VL) learning have allowed the development of multiple models and techniques that offer several impressive implementations, able to currently resolve a variety of tasks that require the collaboration of vision and language. Current datasets used for VL pre-training only contain a limited amount of visual and linguistic knowledge, thus significantly limiting the generalization capabilities of many VL models. External knowledge sources such as knowledge graphs (KGs) and Large Language Models (LLMs) are able to cover such generalization gaps by filling in missing knowledge, resulting in the emergence of hybrid architectures. In the current survey, we analyze tasks that have benefited from such hybrid approaches. Moreover, we categorize existing knowledge sources and types, proceeding to discussion regarding the KG vs LLM dilemma and its potential impact to future hybrid approaches.

Ok-Vqa: A Visual Question Answering Benchmark Requiring External Knowledge

Abstract: Visual Question Answering (VQA) in its ideal form lets us study reasoning in the joint space of vision and language and serves as a proxy for the AI task of scene understanding. However, most VQA benchmarks to date are focused on questions such as simple counting, visual attributes, and object detection that do not require reasoning or knowledge beyond what is in the image. In this paper, we address the task of knowledge-based visual question answering and provide a benchmark, called OK-VQA, where the image content is not sufficient to answer the questions, encouraging methods that rely on external knowledge resources. Our new dataset includes more than 14,000 questions that require external knowledge to answer. We show that the performance of the state-of-the-art VQA models degrades drastically in this new setting. Our analysis shows that our knowledge-based VQA task is diverse, difficult, and large compared to previous knowledge-based VQA datasets. We hope that this dataset enables researchers to open up new avenues for research in this domain.


This would extend an analogous result for causal effects by Richardson et al. from Ref. #REFR (see Rems.

Counterfactuals. An obvious next step, pointed out in Sec.

8.2.3, is to prove completeness of the identification algorithm id-cf for counterfactuals.

This would establish necessity of the diagrammatic identifiability criterion from Cor.

91 and complete the diagrammatic translation of the results by Shpitser et al. from Ref. #OTHEREFR .

Once completeness is established, it is natural to check whether the string diagrammatic treatment of counterfactuals actually allows for an easy argument to the effect that there is no loss of generality in studying the identifiability of counterfactuals based on ADMGs as latent projections.

81 and 93) and fill a gap that seems to currently exist in the conventional literature.

Beyond these, it would be interesting to study whether our approach might naturally extend to cover socalled nested counterfactuals #OTHEREFR by allowing for connections across worlds other than just through the shared background variables U .

Finally, the generalisation of the notion of a counterfactual in Sec.

8.3, which involves the conditioning with fuzzy facts and parallel worlds defined by interventions more general than do-interventions, deserves further exploration.

In particular, one might study how the criteria and methods for the identification of counterfactuals would change in this case.

Causal Models In String Diagrams

Abstract: The framework of causal models, pioneered by Pearl and his collaborators, as well as Spirtes, Glymour and Scheines, provides a principled approach to causal reasoning which is applied today across many scientific domains. Here we present the framework of causal models in the language of string diagrams, interpreted formally using category theory. A class of string diagrams, called network diagrams, are in 1-to-1 correspondence with directed acyclic graphs. A causal model is given by interpreting the components of such a diagram in terms of stochastic maps, functions, or more general channels in a symmetric monoidal category with a 'copy-discard' structure (cd-category). This represents a model as a single mathematical object that can be reasoned with intuitively and yet rigorously. Building on earlier works, most notably by Fong and Jacobs, Kissinger and Zanasi, as well as Fritz and Klingler, this work presents diagrammatic definitions of causal models and functional causal models in a cd-category, which generalise causal Bayesian networks and structural causal models, respectively. We formalise the most general kind of interventions on a model, including but beyond atomic ones described by do-interventions, and present the natural notion of an open causal model, as a causal model 'with inputs'. To apply these to causal reasoning, we also give an approach to conditioning based on a normalisation box, which allows causal inference calculations to be done fully diagrammatically. We use these to define counterfactuals in this setup, and to treat the problems of the identifiability of both causal effects and counterfactuals fully diagrammatically. The benefits of such a presentation of causal models lie both in foundational questions in causal reasoning, and in particular in their clarificatory role and pedagogical value. In fact this manuscript aims to be accessible to different communities, including causal model practitioners as well as researchers in applied category theory. For illustration of the key ideas many examples from the causal model literature are discussed in the diagrammatic language. Overall, we argue and demonstrate that causal reasoning according to the causal model framework is most naturally and intuitively done as diagrammatic reasoning.

Nested Markov Properties For Acyclic Directed Mixed Graphs

Abstract: Directed acyclic graph (DAG) models may be characterized in at least four different ways: via a factorization, the d-separation criterion, the moralization criterion, and the local Markov property. As pointed out by Robins (1986, 1999), Verma and Pearl (1990), and Tian and Pearl (2002b), marginals of DAG models also imply equality constraints that are not conditional independences. The well-known `Verma constraint' is an example. Constraints of this type were used for testing edges (Shpitser et al., 2009), and an efficient marginalization scheme via variable elimination (Shpitser et al., 2011). ::: We show that equality constraints like the `Verma constraint' can be viewed as conditional independences in kernel objects obtained from joint distributions via a fixing operation that generalizes conditioning and marginalization. We use these constraints to define, via Markov properties and a factorization, a graphical model associated with acyclic directed mixed graphs (ADMGs). We show that marginal distributions of DAG models lie in this model, prove that a characterization of these constraints given in (Tian and Pearl, 2002b) gives an alternative definition of the model, and finally show that the fixing operation we used to define the model can be used to give a particularly simple characterization of identifiable causal effects in hidden variable graphical causal models.


Moreover, empirical evaluation of 9 static analysis tools #REFR classified 93% of contracts as vulnerable, thus indicating a considerable number of false positives.

Although such tools are very impressive, and indeed we ourselves use Slither, this reliance on expert rules can make these tools difficult to maintain and update.

We are unaware of any detection tool that detects all known vulnerabilities; or that is easily extendable for future bugs without human developers carefully crafting subtle expert rules and/or hardcoding additional features.

Most smart contract vulnerability analyzers use symbolic execution to reason about all execution paths of a program.

However, symbolic execution can suffer from "path explosion" when the size and complexity of the code increases, leading to significant time and space requirements.

Practical limits on time and space can lead to difficulties analyzing smart contracts at scale.

In addition, only a few vulnerabilities were detected simultaneously that got consensus from four or more tools.

Fuzzing is a dynamic analysis technique, that has the advantage of scaling well to larger programs.

Contractfuzzer #OTHEREFR , and Echidna #OTHEREFR are two notable examples applied to smart contracts.

Rather than relying on a fixed set of pre-defined bug oracles to detect vulnerabilities, fuzzing technique uses sophisticated grammar-based fuzzing campaigns based on a contract API to falsify user-defined predicates or Solidity assertions.

However, generating meaningful inputs for fuzzing typically requires annotating the source code of a contract.

Smart Learning To Find Dumb Contracts

Abstract: We introduce Deep Learning Vulnerability Analyzer (DLVA), a vulnerability detection tool for Ethereum smart contracts based on powerful deep learning techniques for sequential data adapted for bytecode. We train DLVA to judge bytecode even though the supervising oracle, Slither, can only judge source code. DLVA's training algorithm is general: we "extend" a source code analysis to bytecode without any manual feature engineering, predefined patterns, or expert rules. DLVA's training algorithm is also robust: it overcame a 1.25% error rate mislabeled contracts, and-the student surpassing the teacher-found vulnerable contracts that Slither mislabeled. In addition to extending a source code analyzer to bytecode, DLVA is much faster than conventional tools for smart contract vulnerability detection based on formal methods: DLVA checks contracts for 29 vulnerabilities in 0.2 seconds, a speedup of 10-500x+ compared to traditional tools. DLVA has three key components. Smart Contract to Vector (SC2V) uses neural networks to map arbitrary smart contract bytecode to an high-dimensional floating-point vector. Sibling Detector (SD) classifies contracts when a target contract's vector is Euclidian-close to a labeled contract's vector in a training set; although only able to judge 55.7% of the contracts in our test set, it has an average accuracy of 97.4% with a false positive rate of only 0.1%. Lastly, Core Classifier (CC) uses neural networks to infer vulnerable contracts regardless of vector distance. DLVA has an overall accuracy of 96.6% with an associated false positive rate of only 3.7%. We benchmark DLVA's CC with 10 "off-the-shelf" machine learning techniques and show that the CC is more accurate, reducing the average size of the error set by 36.5%. We also benchmark DLVA against well-known alternatives: Oyente, Mythril, Osiris, Smartcheck, Slither, and SoliAudit. DLVA enjoyed meaningfully higher true positive rates while producing comparable false positive rates. Moreover, it did so despite using dramatically less analysis time. Notably, DLVA outperformed state-of-the-art alternatives without using any painstakingly-crafted expert rules or predefined patterns. Table 1: Summary of DLVA results on all datasets analysed (TPR: detection rate; FPR: false alarm rate). Analysed data Test set Acc TPR FPR EthereumSC large [2] 22,634 87.7% 87.3% 11.9% EthereumSC small [3] 1,381 97.6% 95.4% 2.3% SolidiFI benchmark [5] 444 98.6% 100% 1.9% Elysium benchmark [1] 900 99.5% 99.3% 2.2% Reentrancy benchmark [4] 473 99.4% 94.3% 0.0% Average 25,832 96.6% 95.3% 3.7%

Empirical Review Of Automated Analysis Tools On 47,587 Ethereum Smart Contracts

Abstract: Over the last few years, there has been substantial research on automated analysis, testing, and debugging of Ethereum smart contracts. However, it is not trivial to compare and reproduce that research. To address this, we present an empirical evaluation of 9 state-of-the-art automated analysis tools using two new datasets: i) a dataset of 69 annotated vulnerable smart contracts that can be used to evaluate the precision of analysis tools; and ii) a dataset with all the smart contracts in the Ethereum Blockchain that have Solidity source code available on Etherscan (a total of 47,518 contracts). The datasets are part of SmartBugs, a new extendable execution framework that we created to facilitate the integration and comparison between multiple analysis tools and the analysis of Ethereum smart contracts. We used SmartBugs to execute the 9 automated analysis tools on the two datasets. In total, we ran 428,337 analyses that took approximately 564 days and 3 hours, being the largest experimental setup to date both in the number of tools and in execution time. We found that only 42% of the vulnerabilities from our annotated dataset are detected by all the tools, with the tool Mythril having the higher accuracy (27%). When considering the largest dataset, we observed that 97% of contracts are tagged as vulnerable, thus suggesting a considerable number of false positives. Indeed, only a small number of vulnerabilities (and of only two categories) were detected simultaneously by four or more tools.


B. Camera-Lidar Fusion For Relocalization With Multi-Head Self-Attention

To take into account the continuity of the rotation angle #REFR , we present the rotation q as [cos θ, sin θ] T rather than θ.

T and the orientation q = [cos θ, sin θ]

It consists of a position branch and an orientation branch as in #OTHEREFR , #OTHEREFR . Each branch is composed of consecutive MLPs.

In #OTHEREFR , a leaky ReLU activation function was used after each MLP except for the last one in its regression head, but we replace it with the ReLU activation function in our network.

Different from most of the previous studies for end-to-end relocalization, both the position and the orientation are two-dimensional under the assumption that typical serving robots move on planar space.

To our best knowledge, this work is the first study addressing the end-to-end relocalization for a serving robot based on the camera-2D LiDAR fusion in two-dimensional planar space.

Fusionloc: Camera-2D Lidar Fusion Using Multi-Head Self-Attention For End-To-End Serving Robot Relocalization

Abstract: As technology advances in autonomous mobile robots, mobile service robots have been actively used more and more for various purposes. Especially, serving robots have been not surprising products anymore since the COVID-19 pandemic. One of the practical problems in operating serving a robot is that it often fails to estimate its pose on a map that it moves around. Whenever the failure happens, servers should bring the serving robot to its initial location and reboot it manually. In this paper, we focus on end-to-end relocalization of serving robots to address the problem. It is to predict robot pose directly from only the onboard sensor data using neural networks. In particular, we propose a deep neural network architecture for the relocalization based on camera-2D LiDAR sensor fusion. We call the proposed method FusionLoc. In the proposed method, the multi-head selfattention complements different types of information captured by the two sensors to regress the robot pose. Our experiments on a dataset collected by a commercial serving robot demonstrate that FusionLoc can provide better performances than previous endto-end relocalization methods taking only a single image or a 2D LiDAR point cloud as well as a straightforward fusion method concatenating their features.

On The Continuity Of Rotation Representations In Neural Networks

Abstract: In neural networks, it is often desirable to work with various representations of the same space. For example, 3D rotations can be represented with quaternions or Euler angles. In this paper, we advance a definition of a continuous representation, which can be helpful for training deep neural networks. We relate this to topological concepts such as homeomorphism and embedding. We then investigate what are continuous and discontinuous representations for 2D, 3D, and n-dimensional rotations. We demonstrate that for 3D rotations, all representations are discontinuous in the real Euclidean spaces of four or fewer dimensions. Thus, widely used representations such as quaternions and Euler angles are discontinuous and difficult for neural networks to learn. We show that the 3D rotations have continuous representations in 5D and 6D, which are more suitable for learning. We also present continuous representations for the general case of the n-dimensional rotation group SO(n). While our main focus is on rotations, we also show that our constructions apply to other groups such as the orthogonal group and similarity transforms. We finally present empirical results, which show that our continuous rotation representations outperform discontinuous ones for several practical problems in graphics and vision, including a simple autoencoder sanity test, a rotation estimator for 3D point clouds, and an inverse kinematics solver for 3D human poses.


There are a few popular DRL algorithms for solving POMDP such as Deep Recurrent Q-Learning (DRQN) #REFR and RDPG.

As we focus on energy management within one day with T time steps, our task corresponds to a finite horizon POMDP model.

The basic ideas of DRQN and RDPG are to add recurrency to DQN and DDPG algorithms, respectively, by replacing the first fully-connected layer with a recurrent long short-term memory (LSTM) layer.

However, the above DRL algorithms are developed for an infinite horizon setting, while the value function, i.e., critic, and the policy, i.e., actor, are normally dependent on the time steps for the finite horizon case.

Therefore, we propose a novel DRL algorithm named HAFH-RDPG, which adopts a framework that is a combination of dynamic programming and DRL, where RDPG with fixed target is embedded within the framework of finite-horizon value iteration.

In HAFH-RDPG, the finite-horizon value iteration starts from the time step T , and uses backward induction to iteratively derive the value function and optimal policy for each time step t ∈ {T, T − 1, ..., 1}, until it reaches the first time step t = 1.

In each time step, the RDPG algorithm is used to solve a simple one-step POMDP where the target actor and critic networks, i.e., λ t and µ t are fixed to be the trained actor and critic networks of the next time step, i.e., λ t+1 and µ t+1 , which greatly increases stability and performance.

Optimal Scheduling In Iot-Driven Smart Isolated Microgrids Based On Deep Reinforcement Learning

Abstract: In this paper, we investigate the scheduling issue of diesel generators (DGs) in an Internet of Things (IoT)-Driven isolated microgrid (MG) by deep reinforcement learning (DRL). The renewable energy is fully exploited under the uncertainty of renewable generation and load demand. The DRL agent learns an optimal policy from history renewable and load data of previous days, where the policy can generate real-time decisions based on observations of past renewable and load data of previous hours collected by connected sensors. The goal is to reduce operating cost on the premise of ensuring supply-demand balance. In specific, a novel finite-horizon partial observable Markov decision process (POMDP) model is conceived considering the spinning reserve. In order to overcome the challenge of discrete-continuous hybrid action space due to the binary DG switching decision and continuous energy dispatch (ED) decision, a DRL algorithm, namely the hybrid action finite-horizon RDPG (HAFH-RDPG), is proposed. HAFH-RDPG seamlessly integrates two classical DRL algorithms, i.e., deep Q-network (DQN) and recurrent deterministic policy gradient (RDPG), based on a finite-horizon dynamic programming (DP) framework. Extensive experiments are performed with real-world data in an IoT-driven MG to evaluate the capability of the proposed algorithm in handling the uncertainty due to inter-hour and inter-day power fluctuation and to compare its performance with those of the benchmark algorithms.

Deep Recurrent Q-Learning For Partially Observable Mdps

Abstract: Deep Reinforcement Learning has yielded proficient controllers for complex tasks. However, these controllers have limited memory and rely on being able to perceive the complete game screen at each decision point. To address these shortcomings, this article investigates the effects of adding recurrency to a Deep Q-Network (DQN) by replacing the first post-convolutional fully-connected layer with a recurrent LSTM. The resulting \textit{Deep Recurrent Q-Network} (DRQN), although capable of seeing only a single frame at each timestep, successfully integrates information through time and replicates DQN's performance on standard Atari games and partially observed equivalents featuring flickering game screens. Additionally, when trained with partial observations and evaluated with incrementally more complete observations, DRQN's performance scales as a function of observability. Conversely, when trained with full observations and evaluated with partial observations, DRQN's performance degrades less than DQN's. Thus, given the same length of history, recurrency is a viable alternative to stacking a history of frames in the DQN's input layer and while recurrency confers no systematic advantage when learning to play the game, the recurrent net can better adapt at evaluation time if the quality of observations changes.


In this section, we provide details of the problem definition and proposed MTHARS method inspired by SSD #REFR that exploited in the computer vision field.

There are four main components: multiscale window generation and matching, non-maximum suppression, model architecture, and model training.

A Multi-Task Deep Learning Approach For Sensor-Based Human Activity Recognition And Segmentation

Abstract: Sensor-based human activity segmentation and recognition are two important and challenging problems in many real-world applications and they have drawn increasing attention from the deep learning community in recent years. Most of the existing deep learning works were designed based on pre-segmented sensor streams and they have treated activity segmentation and recognition as two separate tasks. In practice, performing data stream segmentation is very challenging. We believe that both activity segmentation and recognition may convey unique information which can complement each other to improve the performance of the two tasks. In this paper, we firstly proposes a new multitask deep neural network to solve the two tasks simultaneously. The proposed neural network adopts selective convolution and features multiscale windows to segment activities of long or short time durations. First, multiple windows of different scales are generated to center on each unit of the feature sequence. Then, the model is trained to predict, for each window, the activity class and the offset to the true activity boundaries. Finally, overlapping windows are filtered out by non-maximum suppression, and adjacent windows of the same activity are concatenated to complete the segmentation task. Extensive experiments were conducted on eight popular benchmarking datasets, and the results show that our proposed method outperforms the state-of-the-art methods both for activity recognition and segmentation.

Ssd: Single Shot Multibox Detector

Abstract: We present a method for detecting objects in images using a single deep neural network. Our approach, named SSD, discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location. At prediction time, the network generates scores for the presence of each object category in each default box and produces adjustments to the box to better match the object shape. Additionally, the network combines predictions from multiple feature maps with different resolutions to naturally handle objects of various sizes. SSD is simple relative to methods that require object proposals because it completely eliminates proposal generation and subsequent pixel or feature resampling stages and encapsulates all computation in a single network. This makes SSD easy to train and straightforward to integrate into systems that require a detection component. Experimental results on the PASCAL VOC, COCO, and ILSVRC datasets confirm that SSD has competitive accuracy to methods that utilize an additional object proposal step and is much faster, while providing a unified framework for both training and inference. For \(300 \times 300\) input, SSD achieves 74.3 % mAP on VOC2007 test at 59 FPS on a Nvidia Titan X and for \(512 \times 512\) input, SSD achieves 76.9 % mAP, outperforming a comparable state of the art Faster R-CNN model. Compared to other single stage methods, SSD has much better accuracy even with a smaller input image size. Code is available at https://github.com/weiliu89/caffe/tree/ssd.


Efficient structural design is a challenge in research, with introduction of the separated convolution #REFR proposed as an effective technique.

Since the introduction of AlexNet #OTHEREFR , there has been an exponential increase in the number of exceptional convolutional neural networks proposed, resulting in promising outcomes for a variety of visual tasks #OTHEREFR .

Despite the remarkable results, deploying CNN models on embedded or mobile devices proves challenging as it poses an immense burden on computation and memory storage.

To address this issue, a significant amount of research has been dedicated to reducing associated costs, thereby making CNN models more practical for real-world applications.

Broadly speaking, this line of research can be categorized into three distinct areas: efficient structure design, network pruning, and network quantization.

This method factorizes the standard convolution into a depthwise and pointwise convolution, reducing computation.

Successful examples of its use in efficient networks include MobileNets #OTHEREFR and ShuffleNets #OTHEREFR .

These networks are widely used on resource-constrained devices and have shown promise in practical applications.

Besides that, various pruning strategies #OTHEREFR have also been proposed to reduce both the computational and storage burdens.

However, these methods often incur accuracy degradation, making them less attractive for practical applications.

Bag Of Tricks With Quantized Convolutional Neural Networks For Image Classification

Abstract: Deep neural networks have been proven effective in a wide range of tasks. However, their high computational and memory costs make them impractical to deploy on resourceconstrained devices. To address this issue, quantization schemes have been proposed to reduce the memory footprint and improve inference speed. While numerous quantization methods have been proposed, they lack systematic analysis for their effectiveness. To bridge this gap, we collect and improve existing quantization methods and propose a gold guideline for post-training quantization. We evaluate the effectiveness of our proposed method with two popular models, ResNet50 and MobileNetV2, on the ImageNet dataset. By following our guidelines, no accuracy degradation occurs even after directly quantizing the model to 8-bits without additional training. A quantization-aware training based on the guidelines can further improve the accuracy in lower-bits quantization. Moreover, we have integrated a multi-stage fine-tuning strategy that works harmoniously with existing pruning techniques to reduce cost even further. Remarkably, our results reveal that a quantized MobileNetV2 with 30% sparsity actually surpasses the performance of the equivalent full-precision model, underscoring the effectiveness and resilience of our proposed scheme.

Mobilenets: Efficient Convolutional Neural Networks For Mobile Vision Applications

Abstract: We present a class of efficient models called MobileNets for mobile and embedded vision applications. MobileNets are based on a streamlined architecture that uses depth-wise separable convolutions to build light weight deep neural networks. We introduce two simple global hyper-parameters that efficiently trade off between latency and accuracy. These hyper-parameters allow the model builder to choose the right sized model for their application based on the constraints of the problem. We present extensive experiments on resource and accuracy tradeoffs and show strong performance compared to other popular models on ImageNet classification. We then demonstrate the effectiveness of MobileNets across a wide range of applications and use cases including object detection, finegrain classification, face attributes and large scale geo-localization.


Instead, #REFR generalizes ideas from NCBC and proposes an algorithm that selects the functional Steiner point of the work function.

where OPT is the offline optimal total path length.

There are many works that combine the Steiner point algorithm for NCBC with existing control methods to perform learning-based online control for LTI systems, e.g., #OTHEREFR , #OTHEREFR , #OTHEREFR .

2) General CBC: For general CBC problems, we can no longer take advantage of the nested property of the convex bodies.

One may consider naively applying NCBC algorithms when the convex bodies happen to be nested and restarting the NCBC algorithm when they are not.

However, due to the myopic nature of NCBC algorithms, which try to remain deep inside of each convex set, they no longer guarantee a competitive ratio when used this way.

Online Stabilization Of Unknown Linear Time-Varying Systems

Abstract: This paper studies the problem of online stabilization of an unknown discrete-time linear time-varying (LTV) system under bounded non-stochastic (potentially adversarial) disturbances. We propose a novel algorithm based on convex body chasing (CBC). Under the assumption of infrequently changing or slowly drifting dynamics, the algorithm guarantees bounded-input-bounded-output stability in the closed loop. Our approach avoids system identification and applies, with minimal disturbance assumptions, to a variety of LTV systems of practical importance. We demonstrate the algorithm numerically on examples of LTV systems including Markov linear jump systems with finitely many jumps.

Chasing Convex Bodies Optimally

Abstract: In the chasing convex bodies problem, an online player receives a request sequence of $N$ convex sets $K_1,\dots, K_N$ contained in a normed space $\mathbb R^d$. The player starts at $x_0\in \mathbb R^d$, and after observing each $K_n$ picks a new point $x_n\in K_n$. At each step the player pays a movement cost of $||x_n-x_{n-1}||$. The player aims to maintain a constant competitive ratio against the minimum cost possible in hindsight, i.e. knowing all requests in advance. The existence of a finite competitive ratio for convex body chasing was first conjectured in 1991 by Friedman and Linial. This conjecture was recently resolved with an exponential $2^{O(d)}$ upper bound on the competitive ratio. ::: In this paper, we drastically improve the exponential upper bound. We give an algorithm achieving competitive ratio $d$ for arbitrary normed spaces, which is exactly tight for $\ell^{\infty}$. In Euclidean space, our algorithm achieves nearly optimal competitive ratio $O(\sqrt{d\log N})$, compared to a lower bound of $\sqrt{d}$. Our approach extends another recent work which chases nested convex bodies using the classical Steiner point of a convex body. We define the functional Steiner point of a convex function and apply it to the work function to obtain our algorithm.


In addition, the central moment discrepancy (CMD) loss #REFR and maximum density divergence (MDD) loss [16] are also proposed to align the feature distributions.

The prevailing UDA methods focus on domain alignment and learning discriminative domain-invariant features via metric learning, domain adversarial training, and optimal transport.

Firstly, the metric learning-based methods aim to reduce the domain discrepancy by learning the domain-invariant feature representations using various metrics.

For instance, some methods [14, 25, 26, #OTHEREFR use the maximum mean discrepancy (MMD) loss to measure the divergence between different domains.

Secondly, the domain adversarial training methods learn the domaininvariant representations to encourage samples from different domains to be non-discriminative with respect to the domain labels via an adversarial loss [13, #OTHEREFR .

The third type of approach aims to minimize the cost transported from the source to the target distribution by finding an optimal coupling cost to mitigate the domain shift [6, 7] .

Unfortunately, these methods are not robust enough for the noisy pseudo target labels for accurate domain alignment.

Different from these mainstream UDA methods and [2] , we interpret the process of UDA as a min-max CE game and find the Nash Equilibria for domain alignment with an intermediate domain and a pure ViT-based solution. Mixup.

It is an effective data augmentation technique to prevent models from over-fitting by linearly interpolating two input data.

Patch-Mix Transformer For Unsupervised Domain Adaptation: A Game Perspective

Abstract: Endeavors have been recently made to leverage the vision transformer (ViT) for the challenging unsupervised domain adaptation (UDA) task. They typically adopt the cross-attention in ViT for direct domain alignment. However, as the performance of cross-attention highly relies on the quality of pseudo labels for targeted samples, it becomes less effective when the domain gap becomes large. We solve this problem from a game theory's perspective with the proposed model dubbed as PMTrans, which bridges source and target domains with an intermediate domain. Specifically, we propose a novel ViT-based module called PatchMix that effectively builds up the intermediate domain, i.e., probability distribution, by learning to sample patches from both domains based on the game-theoretical models. This way, it learns to mix the patches from the source and target domains to maximize the cross entropy (CE), while exploiting two semi-supervised mixup losses in the feature and label spaces to minimize it. As such, we interpret the process of UDA as a min-max CE game with three players, including the feature extractor, classifier, and PatchMix, to find the Nash Equilibria. Moreover, we leverage attention maps from ViT to re-weight the label of each patch by its importance, making it possible to obtain more domain-discriminative feature representations. We conduct extensive experiments on four benchmark datasets, and the results show that PM-Trans significantly surpasses the ViT-based and CNN-based SoTA methods by +3.6% on Office-Home, +1.4% on Office-31, and +17.7% on DomainNet, respectively. https: //vlis2022.github.io/cvpr23/PMTrans

Central Moment Discrepancy (Cmd) For Domain-Invariant Representation Learning

Abstract: The learning of domain-invariant representations in the context of domain adaptation with neural networks is considered. We propose a new regularization method that minimizes the discrepancy between domain-specific latent feature representations directly in the hidden activation space. Although some standard distribution matching approaches exist that can be interpreted as the matching of weighted sums of moments, e.g. Maximum Mean Discrepancy (MMD), an explicit order-wise matching of higher order moments has not been considered before. We propose to match the higher order central moments of probability distributions by means of order-wise moment differences. Our model does not require computationally expensive distance and kernel matrix computations. We utilize the equivalent representation of probability distributions by moment sequences to define a new distance function, called Central Moment Discrepancy (CMD). We prove that CMD is a metric on the set of probability distributions on a compact interval. We further prove that convergence of probability distributions on compact intervals w.r.t. the new metric implies convergence in distribution of the respective random variables. We test our approach on two different benchmark data sets for object recognition (Office) and sentiment analysis of product reviews (Amazon reviews). CMD achieves a new state-of-the-art performance on most domain adaptation tasks of Office and outperforms networks trained with MMD, Variational Fair Autoencoders and Domain Adversarial Neural Networks on Amazon reviews. In addition, a post-hoc parameter sensitivity analysis shows that the new approach is stable w.r.t. parameter changes in a certain interval. The source code of the experiments is publicly available.


For example, Houlsby et al.l #REFR design some adapters with two orders of magnitude fewer parameters to fine-tune compared to full models and achieve similar performance with fine-tuning all parameters of the pre-trained mode.

There are many studies on accelerating fine-tuning process #OTHEREFR . These studies can be roughly categorized into two categories.

The first is to use the knowledge distillation technique to compress large-scale pre-trained language models #OTHEREFR .

#OTHEREFR propose TinyBERT to distill BERT and only use about 28% parameter for natural language understanding.

The second is the adapter-based fine-tuning approach #OTHEREFR , where adapters are new trainable modules added between layers of pre-trained models.

In addition, there are some studies on efficient neural network training from scratch with layer freezing #OTHEREFR . For example, Wang et al.

#OTHEREFR leverage the knowledge from a reference model to accurately evaluate individual layers' training plasticity, freeze the converged ones and unfreeze the frozen layers to continue training.

Our study could motivate researchers to come up with more efficient fine-tuning approaches.

Towards Efficient Fine-Tuning Of Pre-Trained Code Models: An Experimental Study And Beyond

Abstract: Recently, fine-tuning pre-trained code models such as CodeBERT on downstream tasks has achieved great success in many software testing and analysis tasks. While effective and prevalent, fine-tuning the pre-trained parameters incurs a large computational cost. In this paper, we conduct an extensive experimental study to explore what happens to layer-wise pre-trained representations and their encoded code knowledge during fine-tuning. We then propose efficient alternatives to fine-tune the large pre-trained code model based on the above findings. Our experimental study shows that (1) lexical, syntactic and structural properties of source code are encoded in the lower, intermediate, and higher layers, respectively, while the semantic property spans across the entire model. (2) The process of fine-tuning preserves most of the code properties. Specifically, the basic code properties captured by lower and intermediate layers are still preserved during fine-tuning. Furthermore, we find that only the representations of the top two layers change most during fine-tuning for various downstream tasks. (3) Based on the above findings, we propose Telly to efficiently fine-tune pre-trained code models via layer freezing. The extensive experimental results on five various downstream tasks demonstrate that training parameters and the corresponding time cost are greatly reduced, while performances are similar or better. CCS CONCEPTS • Software and its engineering → Software development techniques; Reusability.

Parameter-Efficient Transfer Learning For Nlp

Abstract: Fine-tuning large pre-trained models is an effective transfer mechanism in NLP. However, in the presence of many downstream tasks, fine-tuning is parameter inefficient: an entire new model is required for every task. As an alternative, we propose transfer with adapter modules. Adapter modules yield a compact and extensible model; they add only a few trainable parameters per task, and new tasks can be added without revisiting previous ones. The parameters of the original network remain fixed, yielding a high degree of parameter sharing. To demonstrate adapter's effectiveness, we transfer the recently proposed BERT Transformer model to 26 diverse text classification tasks, including the GLUE benchmark. Adapters attain near state-of-the-art performance, whilst adding only a few parameters per task. On GLUE, we attain within 0.4% of the performance of full fine-tuning, adding only 3.6% parameters per task. By contrast, fine-tuning trains 100% of the parameters per task.