OpenRaiser/Intern-Atlas huggingface dataset:
SLM: End-to-end Feature Selection via Sparse Learnable Masks
Venue: arXiv cs.LG 2023
Year: 2023

Feature selection has been widely used to alleviate compute requirements during training, elucidate model interpretability, and improve model generalizability. We propose SLM -- Sparse Learnable Masks -- a canonical approach for end-to-end feature selection that scales well with respect to both the feature dimension and the number of samples. At the heart of SLM lies a simple but effective learnable sparse mask, which learns which features to select, and gives rise to a novel objective that provably maximizes the mutual information (MI) between the selected features and the labels, which can be derived from a quadratic relaxation of mutual information from first principles. In addition, we derive a scaling mechanism that allows SLM to precisely control the number of features selected, through a novel use of sparsemax. This allows for more effective learning as demonstrated in ablation studies. Empirically, SLM achieves state-of-the-art results against a variety of competitive baselines on eight benchmark datasets, often by a significant margin, especially on those with real-world challenges such as class imbalance.
Data AUDIT: Identifying Attribute Utility- and Detectability-Induced Bias in Task Models
Venue: arXiv cs.LG 2023
Year: 2023

To safely deploy deep learning-based computer vision models for computer-aided detection and diagnosis, we must ensure that they are robust and reliable. Towards that goal, algorithmic auditing has received substantial attention. To guide their audit procedures, existing methods rely on heuristic approaches or high-level objectives (e.g., non-discrimination in regards to protected attributes, such as sex, gender, or race). However, algorithms may show bias with respect to various attributes beyond the more obvious ones, and integrity issues related to these more subtle attributes can have serious consequences. To enable the generation of actionable, data-driven hypotheses which identify specific dataset attributes likely to induce model bias, we contribute a first technique for the rigorous, quantitative screening of medical image datasets. Drawing from literature in the causal inference and information theory domains, our procedure decomposes the risks associated with dataset attributes in terms of their detectability and utility (defined as the amount of information knowing the attribute gives about a task label). To demonstrate the effectiveness and sensitivity of our method, we develop a variety of datasets with synthetically inserted artifacts with different degrees of association to the target label that allow evaluation of inherited model biases via comparison of performance against true counterfactual examples. Using these datasets and results from hundreds of trained models, we show our screening method reliably identifies nearly imperceptible bias-inducing artifacts. Lastly, we apply our method to the natural attributes of a popular skin-lesion dataset and demonstrate its success. Our approach provides a means to perform more systematic algorithmic audits and guide future data collection efforts in pursuit of safer and more reliable models.
ImageEye: Batch Image Processing Using Program Synthesis
Venue: arXiv cs.PL 2023
Year: 2023

This paper presents a new synthesis-based approach for batch image processing. Unlike existing tools that can only apply global edits to the entire image, our method can apply fine-grained edits to individual objects within the image. For example, our method can selectively blur or crop specific objects that have a certain property. To facilitate such fine-grained image editing tasks, we propose a neuro-symbolic domain-specific language (DSL) that combines pre-trained neural networks for image classification with other language constructs that enable symbolic reasoning. Our method can automatically learn programs in this DSL from user demonstrations by utilizing a novel synthesis algorithm. We have implemented the proposed technique in a tool called ImageEye and evaluated it on 50 image editing tasks. Our evaluation shows that ImageEye is able to automate 96% of these tasks.
When do you need Chain-of-Thought Prompting for ChatGPT?
Venue: arXiv cs.AI 2023
Year: 2023

Chain-of-Thought (CoT) prompting can effectively elicit complex multi-step reasoning from Large Language Models~(LLMs). For example, by simply adding CoT instruction ``Let's think step-by-step'' to each input query of MultiArith dataset, GPT-3's accuracy can be improved from 17.7\% to 78.7\%. However, it is not clear whether CoT is still effective on more recent instruction finetuned (IFT) LLMs such as ChatGPT. Surprisingly, on ChatGPT, CoT is no longer effective for certain tasks such as arithmetic reasoning while still keeping effective on other reasoning tasks. Moreover, on the former tasks, ChatGPT usually achieves the best performance and can generate CoT even without being instructed to do so. Hence, it is plausible that ChatGPT has already been trained on these tasks with CoT and thus memorized the instruction so it implicitly follows such an instruction when applied to the same queries, even without CoT. Our analysis reflects a potential risk of overfitting/bias toward instructions introduced in IFT, which becomes more common in training LLMs. In addition, it indicates possible leakage of the pretraining recipe, e.g., one can verify whether a dataset and instruction were used in training ChatGPT. Our experiments report new baseline results of ChatGPT on a variety of reasoning tasks and shed novel insights into LLM's profiling, instruction memorization, and pretraining dataset leakage.
Instruction Tuning with GPT-4
Venue: arXiv cs.CL 2023
Year: 2023

Prior work has shown that finetuning large language models (LLMs) using machine-generated instruction-following data enables such models to achieve remarkable zero-shot capabilities on new tasks, and no human-written instructions are needed. In this paper, we present the first attempt to use GPT-4 to generate instruction-following data for LLM finetuning. Our early experiments on instruction-tuned LLaMA models show that the 52K English and Chinese instruction-following data generated by GPT-4 leads to superior zero-shot performance on new tasks to the instruction-following data generated by previous state-of-the-art models. We also collect feedback and comparison data from GPT-4 to enable a comprehensive evaluation and reward model training. We make our data generated using GPT-4 as well as our codebase publicly available.
A2D: Anywhere Anytime Drumming
Venue: arXiv cs.HC 2023
Year: 2023

The drum kit, which has only been around for around 100 years, is a popular instrument in many music genres such as pop, rock, and jazz. However, the road to owning a kit is expensive, both financially and space-wise. Also, drums are more difficult to move around compared to other instruments, as they do not fit into a single bag. We propose a no-drums approach that uses only two sticks and a smartphone or a webcam to provide an air-drumming experience. The detection algorithm combines deep learning tools with tracking methods for an enhanced user experience. Based on both quantitative and qualitative testing with humans-in-the-loop, we show that our system has zero misses for beginner level play and negligible misses for advanced level play. Additionally, our limited human trials suggest potential directions for future research.
CamJ: Enabling System-Level Energy Modeling and Architectural Exploration for In-Sensor Visual Computing
Venue: arXiv cs.AR 2023
Year: 2023

CMOS Image Sensors (CIS) are fundamental to emerging visual computing applications. While conventional CIS are purely imaging devices for capturing images, increasingly CIS integrate processing capabilities such as Deep Neural Network (DNN). Computational CIS expand the architecture design space, but to date no comprehensive energy model exists. This paper proposes CamJ, a detailed energy modeling framework that provides a component-level energy breakdown for computational CIS and is validated against nine recent CIS chips. We use CamJ to demonstrate three use-cases that explore architectural trade-offs including computing in vs. off CIS, 2D vs. 3D-stacked CIS design, and analog vs. digital processing inside CIS. The code of CamJ is available at: https://github.com/horizon-research/CamJ
ImaGen: A General Framework for Generating Memory- and Power-Efficient Image Processing Accelerators
Venue: arXiv cs.AR 2023
Year: 2023

Image processing algorithms are prime targets for hardware acceleration as they are commonly used in resource- and power-limited applications. Today's image processing accelerator designs make rigid assumptions about the algorithm structures and/or on-chip memory resources. As a result, they either have narrow applicability or result in inefficient designs. This paper presents a compiler framework that automatically generates memory- and power-efficient image processing accelerators. We allow programmers to describe generic image processing algorithms (in a domain specific language) and specify on-chip memory structures available. Our framework then formulates a constrained optimization problem that minimizes on-chip memory usage while maintaining theoretical maximum throughput. The key challenge we address is to analytically express the throughput bottleneck, on-chip memory contention, to enable a lightweight compilation. FPGA prototyping and ASIC synthesis show that, compared to existing approaches, accelerators generated by our framework reduce the on-chip memory usage and/or power consumption by double digits.
Approximate Wireless Communication for Federated Learning
Venue: arXiv cs.DC 2023
Year: 2023

This paper presents an approximate wireless communication scheme for federated learning (FL) model aggregation in the uplink transmission. We consider a realistic channel that reveals bit errors during FL model exchange in wireless networks. Our study demonstrates that random bit errors during model transmission can significantly affect FL performance. To overcome this challenge, we propose an approximate communication scheme based on the mathematical and statistical proof that machine learning (ML) model gradients are bounded under certain constraints. This bound enables us to introduce a novel encoding scheme for float-to-binary representation of gradient values and their QAM constellation mapping. Besides, since FL gradients are error-resilient, the proposed scheme simply delivers gradients with errors when the channel quality is satisfactory, eliminating extensive error-correcting codes and/or retransmission. The direct benefits include less overhead and lower latency. The proposed scheme is well-suited for resource-constrained devices in wireless networks. Through simulations, we show that the proposed scheme is effective in reducing the impact of bit errors on FL performance and saves at least half the time than transmission with error correction and retransmission to achieve the same learning performance. In addition, we investigated the effectiveness of bit protection mechanisms in high-order modulation when gray coding is employed and found that this approach considerably enhances learning performance.
Adoption of AI Technology in the Music Mixing Workflow: An Investigation
Venue: arXiv cs.HC 2023
Year: 2023

The integration of artificial intelligence (AI) technology in the music industry is driving a significant change in the way music is being composed, produced and mixed. This study investigates the current state of AI in the mixing workflows and its adoption by different user groups. Through semi-structured interviews, a questionnaire-based study, and analyzing web forums, the study confirms three user groups comprising amateurs, pro-ams, and professionals. Our findings show that while AI mixing tools can simplify the process and provide decent results for amateurs, pro-ams seek precise control and customization options, while professionals desire control and customization options in addition to assistive and collaborative technologies. The study provides strategies for designing effective AI mixing tools for different user groups and outlines future directions.
TinyDet: Accurate Small Object Detection in Lightweight Generic Detectors
Venue: arXiv cs.CV 2023
Year: 2023

Small object detection requires the detection head to scan a large number of positions on image feature maps, which is extremely hard for computation- and energy-efficient lightweight generic detectors. To accurately detect small objects with limited computation, we propose a two-stage lightweight detection framework with extremely low computation complexity, termed as TinyDet. It enables high-resolution feature maps for dense anchoring to better cover small objects, proposes a sparsely-connected convolution for computation reduction, enhances the early stage features in the backbone, and addresses the feature misalignment problem for accurate small object detection. On the COCO benchmark, our TinyDet-M achieves 30.3 AP and 13.5 AP^s with only 991 MFLOPs, which is the first detector that has an AP over 30 with less than 1 GFLOPs; besides, TinyDet-S and TinyDet-L achieve promising performance under different computation limitation.
Graph Enabled Cross-Domain Knowledge Transfer
Venue: arXiv cs.LG 2023
Year: 2023

To leverage machine learning in any decision-making process, one must convert the given knowledge (for example, natural language, unstructured text) into representation vectors that can be understood and processed by machine learning model in their compatible language and data format. The frequently encountered difficulty is, however, the given knowledge is not rich or reliable enough in the first place. In such cases, one seeks to fuse side information from a separate domain to mitigate the gap between good representation learning and the scarce knowledge in the domain of interest. This approach is named Cross-Domain Knowledge Transfer. It is crucial to study the problem because of the commonality of scarce knowledge in many scenarios, from online healthcare platform analyses to financial market risk quantification, leaving an obstacle in front of us benefiting from automated decision making. From the machine learning perspective, the paradigm of semi-supervised learning takes advantage of large amount of data without ground truth and achieves impressive learning performance improvement. It is adopted in this dissertation for cross-domain knowledge transfer. (to be continued)
Ring-Rotor: A Novel Retractable Ring-shaped Quadrotor with Aerial Grasping and Transportation Capability
Venue: arXiv cs.RO 2023
Year: 2023

This letter presents a novel and retractable ring-shaped quadrotor called Ring-Rotor that can adjust the vehicle's length and width simultaneously. Unlike other morphing quadrotors with high platform complexity and poor controllability, Ring-Rotor uses only one servo motor for morphing but reduces the largest dimension of the vehicle by approximately 31.4\%. It can guarantee passibility while flying through small spaces in its compact form and energy saving in its standard form. Meanwhile, the vehicle breaks the cross configuration of general quadrotors with four arms connected to the central body and innovates a ring-shaped mechanical structure with spare central space. Based on this, an ingenious whole-body aerial grasping and transportation scheme is designed to carry various shapes of objects without the external manipulator mechanism. Moreover, we exploit a nonlinear model predictive control (NMPC) strategy that uses a time-variant physical parameter model to adapt to the quadrotor morphology. Above mentioned applications are performed in real-world experiments to demonstrate the system's high versatility.
GEMINI: Controlling the Sentence-level Writing Style for Abstractive Text Summarization
Venue: arXiv cs.CL 2023
Year: 2023

Human experts write summaries using different techniques, including extracting a sentence from the document and rewriting it, or fusing various information from the document to abstract it. These techniques are flexible and thus difficult to be imitated by any single method. To address this issue, we propose an adaptive model, GEMINI, that integrates a rewriter and a generator to mimic the sentence rewriting and abstracting techniques, respectively. GEMINI adaptively chooses to rewrite a specific document sentence or generate a summary sentence from scratch. Experiments demonstrate that our adaptive approach outperforms the pure abstractive and rewriting baselines on three benchmark datasets, achieving the best results on WikiHow. Interestingly, empirical results show that the human summary styles of summary sentences are consistently predictable given their context. We release our code and model at \url{https://github.com/baoguangsheng/gemini}.
ArmanTTS single-speaker Persian dataset
Venue: arXiv cs.CL 2023
Year: 2023

TTS, or text-to-speech, is a complicated process that can be accomplished through appropriate modeling using deep learning methods. In order to implement deep learning models, a suitable dataset is required. Since there is a scarce amount of work done in this field for the Persian language, this paper will introduce the single speaker dataset: ArmanTTS. We compared the characteristics of this dataset with those of various prevalent datasets to prove that ArmanTTS meets the necessary standards for teaching a Persian text-to-speech conversion model. We also combined the Tacotron 2 and HiFi GAN to design a model that can receive phonemes as input, with the output being the corresponding speech. 4.0 value of MOS was obtained from real speech, 3.87 value was obtained by the vocoder prediction and 2.98 value was reached with the synthetic speech generated by the TTS model.
Deepfake Detection with Deep Learning: Convolutional Neural Networks versus Transformers
Venue: arXiv cs.CR 2023
Year: 2023

The rapid evolvement of deepfake creation technologies is seriously threating media information trustworthiness. The consequences impacting targeted individuals and institutions can be dire. In this work, we study the evolutions of deep learning architectures, particularly CNNs and Transformers. We identified eight promising deep learning architectures, designed and developed our deepfake detection models and conducted experiments over well-established deepfake datasets. These datasets included the latest second and third generation deepfake datasets. We evaluated the effectiveness of our developed single model detectors in deepfake detection and cross datasets evaluations. We achieved 88.74%, 99.53%, 97.68%, 99.73% and 92.02% accuracy and 99.95%, 100%, 99.88%, 99.99% and 97.61% AUC, in the detection of FF++ 2020, Google DFD, Celeb-DF, Deeper Forensics and DFDC deepfakes, respectively. We also identified and showed the unique strengths of CNNs and Transformers models and analysed the observed relationships among the different deepfake datasets, to aid future developments in this area.
Breaking the Envy Cycle: Best-of-Both-Worlds Guarantees for Subadditive Valuations
Venue: arXiv cs.GT 2023
Year: 2023

We study best-of-both-worlds guarantees for the fair division of indivisible items among agents with subadditive valuations. Our main result establishes the existence of a random allocation that is simultaneously ex-ante $\frac{1}{2}$-envy-free, ex-post $\frac{1}{2}$-EFX and ex-post EF1, for every instance with subadditive valuations. We achieve this result by a novel polynomial-time algorithm that randomizes the well-established envy cycles procedure in a way that provides ex-ante fairness. Notably, this is the first best-of-both-worlds fairness guarantee for subadditive valuations, even when considering only EF1 without EFX.
Embodied Concept Learner: Self-supervised Learning of Concepts and Mapping through Instruction Following
Venue: arXiv cs.CV 2023
Year: 2023

Humans, even at a very early age, can learn visual concepts and understand geometry and layout through active interaction with the environment, and generalize their compositions to complete tasks described by natural languages in novel scenes. To mimic such capability, we propose Embodied Concept Learner (ECL) in an interactive 3D environment. Specifically, a robot agent can ground visual concepts, build semantic maps and plan actions to complete tasks by learning purely from human demonstrations and language instructions, without access to ground-truth semantic and depth supervisions from simulations. ECL consists of: (i) an instruction parser that translates the natural languages into executable programs; (ii) an embodied concept learner that grounds visual concepts based on language descriptions; (iii) a map constructor that estimates depth and constructs semantic maps by leveraging the learned concepts; and (iv) a program executor with deterministic policies to execute each program. ECL has several appealing benefits thanks to its modularized design. Firstly, it enables the robotic agent to learn semantics and depth unsupervisedly acting like babies, e.g., ground concepts through active interaction and perceive depth by disparities when moving forward. Secondly, ECL is fully transparent and step-by-step interpretable in long-term planning. Thirdly, ECL could be beneficial for the embodied instruction following (EIF), outperforming previous works on the ALFRED benchmark when the semantic label is not provided. Also, the learned concept can be reused for other downstream tasks, such as reasoning of object states. Project page: http://ecl.csail.mit.edu/
Generative AI for learning: Investigating the potential of synthetic learning videos
Venue: arXiv cs.CV 2023
Year: 2023

Recent advances in generative artificial intelligence (AI) have captured worldwide attention. Tools such as Dalle-2 and ChatGPT suggest that tasks previously thought to be beyond the capabilities of AI may now augment the productivity of creative media in various new ways, including through the generation of synthetic video. This research paper explores the utility of using AI-generated synthetic video to create viable educational content for online educational settings. To date, there is limited research investigating the real-world educational value of AI-generated synthetic media. To address this gap, we examined the impact of using AI-generated synthetic video in an online learning platform on both learners content acquisition and learning experience. We took a mixed-method approach, randomly assigning adult learners (n=83) into one of two micro-learning conditions, collecting pre- and post-learning assessments, and surveying participants on their learning experience. The control condition included a traditionally produced instructor video, while the experimental condition included a synthetic video with a realistic AI-generated character. The results show that learners in both conditions demonstrated significant improvement from pre- to post-learning (p<.001), with no significant differences in gains between the two conditions (p=.80). In addition, no differences were observed in how learners perceived the traditional and synthetic videos. These findings suggest that AI-generated synthetic learning videos have the potential to be a viable substitute for videos produced via traditional methods in online educational settings, making high quality educational content more accessible across the globe.