GenAI4ELab/papercli-papers huggingface dataset:
MAST: Masked Augmentation Subspace Training for Generalizable Self-Supervised Priors
Authors: Chen Huang; Hanlin Goh; Jiatao Gu; Joshua M. Susskind
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Recent Self-Supervised Learning (SSL) methods are able to learn feature representations that are invariant to different data augmentations, which can then be transferred to downstream tasks of interest. However, different downstream tasks require different invariances for their best performance, so the optimal choice of augmentations for SSL depends on the target task. In this paper, we aim to learn self-supervised features that generalize well across a variety of downstream tasks (e.g., object classification, detection and instance segmentation) without knowing any task information beforehand. We do so by Masked Augmentation Subspace Training (or MAST) to encode in the single feature space the priors from different data augmentations in a factorized way. Specifically, we disentangle the feature space into separate subspaces, each induced by a learnable mask that selects relevant feature dimensions to model invariance to a specific augmentation. We show the success of MAST in jointly capturing generalizable priors from different augmentations, using both unique and shared features across the subspaces. We further show that MAST benefits from uncertainty modeling to reweight ambiguous samples from strong augmentations that may cause similarity mismatch in each subspace. Experiments demonstrate that MAST consistently improves generalization on various downstream tasks, while being task-agnostic and efficient during SSL. We also provide interesting insights about how different augmentations are related and how uncertainty reflects learning difficulty.
Understanding Incremental Learning of Gradient Descent: A Fine-grained analysis of Matrix Sensing
Authors: Jikai Jin; Zhiyuan Li; Kaifeng Lyu; Simon Shaolei Du; Jason D. Lee
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: The implicit bias of optimization algorithms such as gradient descent (GD) is believed to play an important role in generalization of modern machine learning methods such as deep learning. This paper provides a fine-grained analysis of the dynamics of GD for the matrix sensing problem, whose goal is to recover a low-rank ground-truth matrix from near-isotropic linear measurements. With small initialization, we that GD behaves similarly to the greedy low-rank learning heuristics~\citep{li2020towards} and follows an incremental learning procedure~\citep{gissin2019implicit}. That is, GD sequentially learns solutions with increasing ranks until it recovers the ground-truth matrix. Compared to existing works which only analyze the first learning phase for rank-1 solutions, our result is stronger because it characterizes the whole learning process. Moreover, our analysis of the incremental learning procedure applies to the
under-parameterized regime as well. As a key ingredient of our analysis, we observe that GD always follows an approximately low-rank trajectory and develops novel landscape properties for matrix sensing with low-rank parameterization. Finally, we conduct numerical experiments which confirm our theoretical findings.
Lossless Adaptation of Pretrained Vision Models For Robotic Manipulation
Authors: Mohit Sharma; Claudio Fantacci; Yuxiang Zhou; Skanda Koppula; Nicolas Heess; Jon Scholz; Yusuf Aytar
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Recent works have shown that large models pretrained on common visual learning tasks can provide useful representations for a wide range of specialized perception problems, as well as a variety of robotic manipulation tasks.  While prior work on robotic manipulation has predominantly used frozen pretrained features, we demonstrate that in robotics this approach can fail to reach optimal performance, and that fine-tuning of the full model can lead to significantly better results. Unfortunately, fine-tuning disrupts the pretrained visual representation, and causes representational drift towards the fine-tuned task thus leading to a loss of the versatility of the original model. We introduce a method for lossless adaptation to address this shortcoming of classical fine-tuning. We demonstrate that appropriate placement of our parameter efficient adapters can significantly reduce the performance gap between frozen pretrained representations and full end-to-end fine-tuning without changes to the original representation and thus preserving original capabilities of the pretrained model. We perform a comprehensive investigation across three major model architectures (ViTs, NFNets, and ResNets), supervised (ImageNet-1K classification) and self-supervised pretrained weights (CLIP, BYOL, Visual MAE) in three manipulation task domains and 35 individual tasks, and demonstrate that our claims are strongly validated in various settings. Please see real world videos at https://sites.google.com/view/robo-adapters
Dual Diffusion Implicit Bridges for Image-to-Image Translation
Authors: Xuan Su; Jiaming Song; Chenlin Meng; Stefano Ermon
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Common image-to-image translation methods rely on joint training over data from both source and target domains. The training process requires concurrent access to both datasets, which hinders data separation and privacy protection; and existing models cannot be easily adapted for translation of new domain pairs. We present Dual Diffusion Implicit Bridges (DDIBs), an image translation method based on diffusion models, that circumvents training on domain pairs. Image translation with DDIBs relies on two diffusion models trained independently on each domain, and is a two-step process: DDIBs first obtain latent encodings for source images with the source diffusion model, and then decode such encodings using the target model to construct target images. Both steps are defined via ordinary differential equations (ODEs), thus the process is cycle consistent only up to discretization errors of the ODE solvers. Theoretically, we interpret DDIBs as concatenation of source to latent, and latent to target Schrodinger Bridges, a form of entropy-regularized optimal transport, to explain the efficacy of the method. Experimentally, we apply DDIBs on synthetic and high-resolution image datasets, to demonstrate their utility in a wide variety of translation tasks and their inherent optimal transport properties.
CommsVAE: Learning the brain's macroscale communication dynamics using coupled sequential VAEs
Authors: Eloy Geenjaar; Noah Lewis; Amrit Kashyap; Robyn Miller; Vince Calhoun
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Communication within or between complex systems is commonplace in the natural sciences and fields such as graph neural networks. The brain is a perfect example of such a complex system, where communication between brain regions is constantly being orchestrated. To analyze communication, the brain is often split up into anatomical regions that each perform certain computations. These regions must interact and communicate with each other to perform tasks and support higher-level cognition. On a macroscale, these regions communicate through signal propagation along the cortex and along white matter tracts over longer distances. When and what types of signals are communicated over time is an unsolved problem and is often studied using either functional or structural data. In this paper, we propose a non-linear generative approach to communication from functional data. We address three issues with common connectivity approaches by explicitly modeling the directionality of communication, finding communication at each timestep, and encouraging sparsity. To evaluate our model, we simulate temporal data that has sparse communication between nodes embedded in it and show that our model can uncover the expected communication dynamics. Subsequently, we apply our model to temporal neural data from multiple tasks and show that our approach models communication that is more specific to each task. The specificity of our method means it can have an impact on the understanding of psychiatric disorders, which are believed to be related to highly specific communication between brain regions compared to controls. In sum, we propose a general model for dynamic communication learning on graphs, and show its applicability to a subfield of the natural sciences, with potential widespread scientific impact.
Toxicity in Multilingual Machine Translation at Scale
Authors: Marta R. Costa-jussà; Christophe Ropers; Eric Michael Smith; Daniel Licht; Carlos Escolano; Javier Ferrando
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Machine Translation systems can produce different types of errors, some of which get characterized as critical or catastrophic due to the specific negative impact they can have on users. Automatic or human evaluation metrics do not necessarily differentiate between such critical errors and more innocuous ones. In this paper we focus on one type of critical error: added toxicity. We evaluate and analyze added toxicity when translating a large evaluation dataset (HOLISTICBIAS, over 472k sentences, covering 13 demographic axes) from English into 164 languages. The toxicity automatic evaluation shows that added toxicity across languages varies from 0% to 5%. The output languages with the most added toxicity tend to be low-resource ones, and the demographic axes with the most added toxicity include sexual orientation, gender and sex, and ability. We also perform human evaluation on a subset of 8 directions, confirming the prevalence of true
added toxicity.

We use a measurement of the amount of source contribution to the translation, where a low source contribution implies hallucination, to interpret what causes toxicity. We observe that the source contribution is somewhat correlated with toxicity but that 45.6% of added toxic words have a high source contribution, suggesting that much of the added toxicity may be due to mistranslations. Combining the signal of source contribution level with a measurement of translation robustness allows us to flag 22.3% of added toxicity, suggesting that added toxicity may be related to both hallucination and the stability of translations in different contexts. Given these findings, our recommendations to reduce added toxicity are to curate training data to avoid mistranslations, mitigate hallucination and check unstable translations.
Proportional Amplitude Spectrum Training Augmentation for Synthetic-to-Real Domain Generalization
Authors: Prithvijit Chattopadhyay; Kartik Sarangmath; Vivek Vijaykumar; Judy Hoffman
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Synthetic data offers the promise of cheap and bountiful training data for settings where lots of labeled real-world data for some task is unavailable. However, models trained on synthetic data significantly underperform on real-world data. In this paper, we propose Proportional Amplitude Spectrum Training Augmentation (PASTA), a simple and effective augmentation strategy to improve out-of-the-box synthetic-to-real (syn-to-real) generalization performance. PASTA involves perturbing the amplitude spectrums of the synthetic images in the Fourier domain to generate augmented views. We design PASTA to perturb the amplitude spectrums in a structured manner such that high-frequency components are perturbed relatively more than the low-frequency ones. For the tasks of semantic segmentation (GTAV→Real), object detection (Sim10K→Real), and object recognition (VisDAC Syn→Real), across a total of 5 syn-to-real shifts, we find that PASTA  either outperforms or is consistently competitive with more complex state-of-the-art methods while being complementary to other generalization approaches.
Explaining RL Decisions with Trajectories
Authors: Shripad Vilasrao Deshmukh; Arpan Dasgupta; Balaji Krishnamurthy; Nan Jiang; Chirag Agarwal; Georgios Theocharous; Jayakumar Subramanian
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Explanation is a key component for the adoption of reinforcement learning (RL) in many real-world decision-making problems. In the literature,  the explanation is often provided by saliency attribution to the features of the RL agent's state. In this work, we propose a complementary approach to these explanations, particularly for offline RL, where we attribute the policy decisions of a trained RL agent to the trajectories encountered by it during training. To do so, we encode trajectories in offline training data individually as well as collectively (encoding a set of trajectories). We then attribute policy decisions to a set of trajectories in this encoded space by estimating the sensitivity of the decision with respect to that set.  Further, we demonstrate the effectiveness of the proposed approach in terms of quality of attributions as well as practical scalability in diverse environments that involve both discrete and continuous state and action spaces such as grid-worlds, video games (Atari) and continuous control (MuJoCo). We also conduct a human study on a simple navigation task to observe how their understanding of the task compares with data attributed for a trained RL policy.
ACAT: Adversarial Counterfactual Attention for Classification and Detection in Medical Imaging
Authors: Alessandro Fontanella; Antreas Antoniou; Wenwen Li; Joanna Wardlaw; Grant Mair; Emanuele Trucco; Amos Storkey
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: In some medical imaging tasks and other settings where only small parts of the image are informative for the classification task, traditional CNNs can sometimes struggle to generalise. Manually annotated Regions of Interest (ROI) are sometimes used to isolate the most informative parts of the image. However, these are expensive to collect and may vary significantly across annotators. To overcome these issues, we propose a method to generate saliency maps, obtained from adversarially generated counterfactual images. With this method, we are able to isolate the area of interest in brain and lung CT scans without using any manual annotations. Our saliency maps, in the task of localising the lesion location out of 6 possible regions, obtain a score of $65.05 \%$ on brain CT scans, improving the score of $61.29 \%$ obtained with the best competing method. We also employ the saliency maps in a framework that refines a classifier pipeline. In particular, the saliency maps are used to obtain soft spatial attention masks that modulate the image features at different scales. We refer to our method as \emph{Adversarial Counterfactual Attention} (ACAT). ACAT increases the baseline classification accuracy of lesions in brain CT scans from $71.39 \%$ to $72.55 \%$ and of COVID-19 related findings in lung CT scans from $67.71 \%$ to $70.84 \%$ and exceeds the performance of competing methods.
Machine Learning Force Fields with Data Cost Aware Training
Authors: Alexander Bukharin; Tianyi Liu; Shengjie Wang; Simiao Zuo; Weihao Gao; Wen Yan; Tuo Zhao
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Machine learning force fields (MLFF) have been proposed to accelerate molecular dynamics (MD) simulation, which finds widespread applications in chemistry and biomedical research. Even for the most data-efficient MLFF models, reaching chemical accuracy can require hundreds of frames of force and energy labels generated by expensive quantum mechanical algorithms, which may scale as $O(n^3)$ to $O(n^7)$, with $n$ being the number of basis functions used and typically proportional to the number of atoms.
To address this issue, we propose a multi-stage computational framework -- ASTEROID, which enjoys low training data generation cost without significantly sacrificing MLFFs' accuracy. Specifically, ASTEROID leverages a combination of both large cheap inaccurate data and small expensive accurate data. The motivation behind ASTEROID is that inaccurate data, though incurring large bias, can help capture the sophisticated structures of the underlying force field. Therefore, we first train a MLFF model on a large amount of inaccurate training data, employing a bias-aware loss function to prevent the model from overfitting the potential bias of the inaccurate training data. We then fine-tune the obtained model using a small amount of accurate training data, which preserves the knowledge learned from the inaccurate training data while significantly improving the model's accuracy. Moreover, we propose a variant of ASTEROID based on score matching for the setting where the inaccurate training data are unlabelled. Extensive experiments on MD simulation datasets show that ASTEROID can significantly reduce data generation costs while improving the accuracy of MLFFs.
Avoiding spurious correlations via logit correction
Authors: Sheng Liu; Xu Zhang; Nitesh Sekhar; Yue Wu; Prateek Singhal; Carlos Fernandez-Granda
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Empirical studies suggest that machine learning models trained with empirical risk minimization (ERM) often rely on attributes that may be spuriously correlated with the class labels. Such models typically lead to poor performance during inference for data lacking such correlations. In this work, we explicitly consider a situation where potential spurious correlations are present in the majority of training data. In contrast with existing approaches, which use the ERM model outputs to detect the samples without spurious correlations and either heuristically upweight or upsample those samples, we propose the logit correction (LC) loss, a simple yet effective improvement on the softmax cross-entropy loss, to correct the sample logit. We demonstrate that minimizing the LC loss is equivalent to maximizing the group-balanced accuracy, so the proposed LC could mitigate the negative impacts of spurious correlations. Our extensive experimental results further reveal that the proposed LC loss outperforms state-of-the-art solutions on multiple popular benchmarks by a large margin, an average 5.5% absolute improvement, without access to spurious attribute labels. LC is also competitive with oracle methods that make use of the attribute labels.
Context and History Aware Other-Shaping
Authors: Akbir Khan; Newton Kwan; Timon Willi; Chris Lu; Andrea Tacchetti; Jakob Nicolaus Foerster
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Cooperation failures, in which self-interested agents converge to collectively worst-case outcomes, are a common failure mode of Multi-Agent Reinforcement Learning (MARL) methods. Methods such as Model-Free Opponent Shaping (MFOS) and The Good Shepherd address this issue by shaping their co-player’s learning into mutual cooperation. However, these methods fail to capture important co-player learning dynamics or do not scale to co-players parameterised by deep neural networks. To address these issues, we propose Context and History Aware Other-Shaping (CHAOS). A CHAOS agent is a meta-learner parameterised by a recurrent neural network that learns to shape its co-player over multiple trials. CHAOS considers both the context (inter-episode information), and history (intra-episode information) to shape co-players successfully. CHAOS also successfully scales to shaping co-players parameterised by deep neural networks. In a set of experiments, we show that CHAOS achieves state-of-the-art shaping in matrix games. We provide extensive ablations, motivating the importance of both context and history. CHAOS also successfully shapes on a complex grid-worldbased game, demonstrating CHAOS’s scalability empirically. Finally, we provide empirical evidence that, counterintuitively, the widely-used Coin Game environment does not require history to learn shaping because states are often indicative of past actions. This suggests that the Coin Game is, in contrast to common understanding, unsuitable for investigating shaping in high-dimensional, multi-step environments.
Worst-case Few-shot Evaluation: Are Neural Networks Robust Few-shot Learners?
Authors: Yudong Wang; Ma Chang; Qingxiu Dong; Lingpeng Kong; Zhifang Sui; Jingjing Xu
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Neural networks have achieved remarkable performance on various few-shot tasks. However, recent studies reveal that existing few-shot models often exploit the spurious correlations between training and test sets, achieving a high performance that is hard to generalize. Motivated that a robust few-shot learner should accurately classify data given any valid training set, we consider a worst-case few-shot evaluation that computes worst-case generalization errors by constructing a challenging few-shot set. Specifically, we search for the label-balanced subset of a full-size training set that results in the largest expected risks. Since the search space is enormous, we propose an efficient method NMMD-attack to optimize the target by maximizing NMMD distance (maximum mean discrepancy based on neural tangent kernel). Experiments show that NMMD-attack can successfully attack various architectures. The large gap between average performance and worst-case performance shows that neural networks still suffer from poor robustness. We appeal to more worst-case benchmarks for better robust few-shot evaluation.
SoundCount: Sound Counting from Raw Audio with Dyadic Decomposition Neural Network
Authors: Yuhang He; Zhuangzhuang Dai; Niki Trigoni; Andrew Markham
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: In this paper, we study an underexplored, yet important and challenging problem: counting the number of distinct sound events in data characterized by a high degree of polyphonicity and spectral overlap. A key example is counting individual bird calls in bioacoustic data, from which biodiversity can be estimated. We do so by systematically proposing a novel end-to-end trainable neural network, designing new evaluation protocols, quantifying the difficulty of counting depending on sound polyphonicity, and creating a new dataset tailored for concurrent sound event counting. Unlike existing methods that all apply frequency-selective filters on the raw waveform in a one-stage manner, our neural network progressively decomposes the raw waveform dyadically in frequency domain. Taking inspiration from wavelet decomposition, intermediate waveforms convolved by a parent filter are successively processed by a pair of children filters that evenly split the parent filter's carried frequency response. An energy gain normalization module is introduced to normalize received sound events' loudness variance and spectrum overlap. The network is fully convolutional and parameter-frugal so it is light-weight and computationally efficient. We further design a set of polyphony-aware metrics to quantify sound counting difficulty level from different perspectives. To show the efficiency and generalization of our method (we call DyDecNet), we do experiments on both bioacoustic bird sound (both synthetic and real-world sound), telephone-ring sound and music sound data. Comprehensive experiment results show our method outperforms existing sound event detection (SED) methods significantly. The dyadic decomposition front-end network can be used by existing methods to improve their performance accordingly.