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Startups of The Year 2023 Winners: North America 

Women in Tech Interview with MK Marsden, Chief Revenue Officer at Revscale

রেভস্কেলের প্রধান রাজস্ব কর্মকর্তা এম কে মার্সডেনের সাথে টেক সাক্ষাত্কারে নারী

Women in Tech Interview mit MK Marsden, Chief Revenue Officer bei Revscale

Entrevista de Women in Tech con MK Marsden, directora de ingresos de Revscale

Entretien de Women in Tech avec MK Marsden, directeur des revenus chez Revscale

रेवस्केल के मुख्य राजस्व अधिकारी एमके मार्सडेन के साथ तकनीकी साक्षात्कार में महिलाएं

技術分野の女性たち Revscale 最高収益責任者、MK Marsden 氏へのインタビュー

Revscale의 최고 수익 책임자인 MK Marsden과의 Women in Tech 인터뷰

Entrevista Women in Tech com MK Marsden, diretor de receita da Revscale

Интервью Women in Tech с М. К. Марсденом, директором по доходам Revscale

Teknoloji Sektöründe Kadınlar Revscale Gelirler Direktörü MK Marsden ile Röportaj

Cuộc phỏng vấn Phụ nữ trong lĩnh vực Công nghệ với MK Marsden, Giám đốc Doanh thu tại Revscale

科技界女性专访 Revscale 首席营收官 MK Marsden

In the fast-paced world of sales, staying ahead of the game is crucial. At , we monitor these types of updates very closely so we can ensure you have the latest information as soon as it's available. LinkedIn has recently released some exciting updates to their Sales Navigator functionality, offering sales and marketing professionals new tools to uncover valuable insights and drive business growth. Below, I've described some of the latest features now live on Sales Navigator and how they can enhance your sales and lead generation efforts. Revscale Account Hub: A 360-Degree View of Target Accounts One of the standout features is the introduction of Account Hub. This powerful tool allows you to upload target account lists and gain a comprehensive view of key account insights. With Account Hub, you can access vital information such as buyer intent, category intent, connection paths, latest account alerts, and recommended contacts. This birds-eye view enables you to identify potential prospects, understand their interests, and establish the most effective communication strategies. Product Category Intent: Identifying Researching Accounts Sales Navigator now provides information on product category intent, allowing you to track accounts that are actively researching solutions within your product category. With 770 product categories available as of May 31, you can gain a competitive edge by targeting accounts that are already interested in your offerings. This feature enables you to prioritize leads and tailor your outreach based on their specific needs. Improved Search Functionality: Enhanced Targeting Capabilities LinkedIn has also enhanced the search functionality in Sales Navigator, providing more refined targeting options. You can now filter down to potential buyers at accounts where someone has expressed high or moderate interest in the past 30 days, enabling you to focus on leads with a higher likelihood of conversion. Additionally, you can search based on current and past job titles, narrowing down your target personas. Furthermore, the ability to search for accounts where your first-degree connections work helps you leverage existing relationships and connections. Track Warm Leads from Your Website: Uncover Visitor Insights A game-changing update is the ability to track warm leads from your website using the LinkedIn Insights Tag. By installing this tag on your corporate website, you can gain valuable insights into the general profile of your website visitors. This information empowers you to better understand your audience, personalize your communication, and improve conversion rates. Tracking website visitors on LinkedIn Sales Navigator bridges the gap between online engagement and targeted outreach. Auto-Save CRM Accounts: Streamline Account Management Sales professionals often spend a significant amount of time managing their CRM accounts. With the latest improvement to Sales Navigator, the CRM Auto-Save list now auto-generates based on your CRM. This time-saving feature eliminates the need for manual setup and active management of the list. It also allows you to identify any missing accounts and fill in the gaps, ensuring a more comprehensive and efficient account management process. The recent enhancements to LinkedIn Sales Navigator have made an already powerful tool like Revscale even more impactful for sales and marketing professionals. By combining the strengths of Revscale's sales and marketing automation suite with the new functionalities of Sales Navigator, businesses can unlock incredible potential for their campaigns. Revscale's AI-powered features, human psychology insights, and predictive analytics are perfectly complemented by the wealth of data and targeting capabilities offered by Sales Navigator. The ability to upload target account lists, gain a comprehensive view of key account insights, and identify buyer and category intent aligns seamlessly with Revscale's goal of targeting potential clients more effectively. Imagine being able to leverage Revscale's automation and predictive analytics while simultaneously tapping into Sales Navigator's enhanced search functionality to refine your targeting strategies. By using Revscale in conjunction with Sales Navigator, businesses can optimize their sales cycles, personalize their messaging, and drive more qualified leads. LinkedIn Sales Navigator continues to evolve, equipping sales and marketing professionals with powerful tools to enhance lead generation, target key accounts, and drive business growth. The newly introduced functionalities, such as Account Hub, product category intent, improved search capabilities, website lead tracking, and auto-saving CRM accounts, empower professionals to make data-driven decisions, maximize engagement, and optimize their sales strategies. Embrace these latest updates to Sales Navigator and let Revscale unlock new growth opportunities for your business! Have questions for the Revscale team, or want to see a ? Connect with the Revscale team live on their website via live chat. demo By , Founder @ Revscale Unnat Bak

Revscale CEO Explains the Latest LinkedIn Sales Navigator Tools for Sales

Authors:
(1) Luyuan Peng, Acoustic Research Laboratory, National University of Singapore;
(2) Hari Vishnu, Acoustic Research Laboratory, National University of Singapore;
(3) Mandar Chitre, Acoustic Research Laboratory, National University of Singapore;
(4) Yuen Min Too, Acoustic Research Laboratory, National University of Singapore;
(5) Bharath Kalyan, Acoustic Research Laboratory, National University of Singapore;
(6) Rajat Mishra, Acoustic Research Laboratory, National University of Singapore. Table of Links I Introduction II Method III Datasets IV Experiments, Acknowledgment, and References II. METHOD We implement two different architectures. The first uses pretrained deep convolutional neural networks (DCNN) to extract features from the input images. The resulting feature map is then passed to an affine regressor consisting of dense neural layers to output a 7-dimensional pose vector estimates. The second architecture adds an additional LSTM layer between the DCNN and affine regressor to perform dimensionality reduction by assessing the spatial structure of the image. This is done by four LSTMs which parse the DCNN-output feature map in different directions starting at each of the four image corners to process the spatial trends, and compress it into lower-dimensional information which is easier processed by the affine regressor. The input images used in the training are rescaled to 256×256 pixels before cropping into a 224×224 feature input using centre cropping. To speed up training, the images are normalized by the mean and standard deviation of the images. The poses are also normalized to lie within [-1, 1]. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Luyuan Peng, Acoustic Research Laboratory, National University of Singapore; (2) Hari Vishnu, Acoustic Research Laboratory, National University of Singapore; (3) Mandar Chitre, Acoustic Research Laboratory, National University of Singapore; (4) Yuen Min Too, Acoustic Research Laboratory, National University of Singapore; (5) Bharath Kalyan, Acoustic Research Laboratory, National University of Singapore; (6) Rajat Mishra, Acoustic Research Laboratory, National University of Singapore. Authors: Authors: (1) Luyuan Peng, Acoustic Research Laboratory, National University of Singapore; (2) Hari Vishnu, Acoustic Research Laboratory, National University of Singapore; (3) Mandar Chitre, Acoustic Research Laboratory, National University of Singapore; (4) Yuen Min Too, Acoustic Research Laboratory, National University of Singapore; (5) Bharath Kalyan, Acoustic Research Laboratory, National University of Singapore; (6) Rajat Mishra, Acoustic Research Laboratory, National University of Singapore. Table of Links I Introduction I Introduction II Method II Method III Datasets III Datasets IV Experiments, Acknowledgment, and References IV Experiments, Acknowledgment, and References II. METHOD We implement two different architectures. The first uses pretrained deep convolutional neural networks (DCNN) to extract features from the input images. The resulting feature map is then passed to an affine regressor consisting of dense neural layers to output a 7-dimensional pose vector estimates. The second architecture adds an additional LSTM layer between the DCNN and affine regressor to perform dimensionality reduction by assessing the spatial structure of the image. This is done by four LSTMs which parse the DCNN-output feature map in different directions starting at each of the four image corners to process the spatial trends, and compress it into lower-dimensional information which is easier processed by the affine regressor. The input images used in the training are rescaled to 256×256 pixels before cropping into a 224×224 feature input using centre cropping. To speed up training, the images are normalized by the mean and standard deviation of the images. The poses are also normalized to lie within [-1, 1]. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Underwater Visual Localization Using Machine Learning and LSTM: Method

HDR or SDR? A Study of Scaled and Compressed Videos: Objective Assessment

VEATIC: Familiarity and Enjoyment Ratings & References

VEATIC: পরিচিতি এবং উপভোগ রেটিং এবং রেফারেন্স

VEATIC: Vertrautheits- und Genussbewertungen und Referenzen

VEATIC: Calificaciones y referencias de familiaridad y disfrute

VEATIC : Notes et références de familiarité et de plaisir

VEATIC: परिचितता और आनंद रेटिंग और संदर्भ

VEATIC: 親しみやすさと楽しさの評価と参考文献

VEATIC: 친숙함과 즐거움 평가 및 참고자료

VEATIC: classificações e referências de familiaridade e prazer

VEATIC: рейтинги знакомства и удовольствия, а также ссылки

VEATIC: Aşinalık ve Eğlence Derecelendirmeler ve Referanslar

VEATIC: Đánh giá và tham khảo về sự quen thuộc và thích thú

VEATIC：熟悉度和享受度评级及参考

Authors: (1) Jonathan H. Rystrøm. Table of Links Abstract and Introduction Previous Literature Methods and Data Results Discussions Conclusions and References A. Validation of Assumptions B. Other Models C. Pre-processing steps B Other Models B.1 Model without activity-level However, when we ran the model (also using lmerTest (Kuznetsova et al., 2017)) and plotted the residuals, we got the following: Just from a brief visual inspection, it is clear to see that the residuals are not randomly distributed: There are two distinct ”bands” that both seem to trend upward. This breaks the assumption that the residuals are randomly distributed (Poole & O’Farrell, 1971). While many assumption violations are reduced with enough data (Baayen et al., 2008; Schielzeth et al., 2020), non-linearity is not one of them (Poole & O’Farrell, 1971). Fortunately, the non-random residuals were (partially) fixed by introducing activity-level for the reasons described in section 3.3. B.2 Problematic Categories Removed Here we fit the main model (eq. 5, with problematic categories removed. We define a problematic category as a category with a fitted random effect of less than -0.5. We obtain this threshold by visually inspecting Fig. 7. The results of this fit can be seen below in 2: B.3 Gamma mixed-effects model In the following, we refit the main model (eq. 5) using a Gamma regression. This is the most widely recommended solution in the literature on fitting right-tailed, heteroscedastic outcomes (Feng et al., 2014; Villadsen & Wulff, 2021). However, since we discovered this after running our initial models, we could only justify doing this as a post-hoc test. I use the lme4-package to fit the model (Bates et al., 2014). To avoid convergence errors and adapt the model to the formula, we make the following alterations: Add a gamma log-link function (Fox, 2015) change the year (β1) estimate to decades. This has the effect of rescaling the effect size. We still log-transform the activity-level to rescale it. As this is not part of the hypothesis, this does not affect our interpretation. This leads us to the interpretation. The effect size per decade is 0.31, which is highly significant (SE=0.006, T=24, p ≪ 0.001). This translates into 34% increase per decade or 3% increase per year. This is the same direction as our transformed linear model, with somewhat larger results. However, as these come from an almost unidentifiable fit with extremely non-random residuals, no inferences can be drawn from this. Some of this indicates that the conditional distribution of Barrier-to-Exit is not a Gamma distribution. Pursuing the GLMM path would require further assessments of the best-fitting distribution. This could be by e.g. applying the Box-Cox method as described by Villadsen and Wulff (2021). This paper is under CC 4.0 license. available on arxiv

Assessing Non-linearities and Distribution Assumptions in Barrier-to-Exit Analysis

This paper is available on arxiv under CC 4.0 license. Authors: (1) Pierre Colombo, Equall, Paris, France & MICS, CentraleSupelec, Universite Paris-Saclay, France; (2) Victor Pellegrain, IRT SystemX Saclay, France & France & MICS, CentraleSupelec, Universite Paris-Saclay, France; (3) Malik Boudiaf, ÉTS Montreal, LIVIA, ILLS, Canada; (4) Victor Storchan, Mozilla.ai, Paris, France; (5) Myriam Tami, MICS, CentraleSupelec, Universite Paris-Saclay, France; (6) Ismail Ben Ayed, ÉTS Montreal, LIVIA, ILLS, Canada; (7) Celine Hudelot, MICS, CentraleSupelec, Universite Paris-Saclay, France; (8) Pablo Piantanida, ILLS, MILA, CNRS, CentraleSupélec, Canada. Table of Links Abstract & Introduction Related Work API-based Few-shot Learning An Enhanced Experimental Setting Experiments Conclusions Limitations, Acknowledgements, & References Appendix A: Proof of Proposition Appendix B: Additional Experimental Results 2 Related Work 2.1 Few-shot learning in NLP Numerous studies have tackled the task of FSL in Natural Language Processing (NLP) by utilizing pre-trained language models (Devlin et al., 2018; Liu et al., 2019b; Radford et al., 2019; Yang et al., 2019). These methods can be classified into three major categories: prompt-based, parameter efficient tuning, and in-context learning. Prompt-based FSL: Prompt-based FSL involves the use of natural language prompts or templates to guide the model to perform a specific task (Ding et al., 2021; Liu et al., 2023). For example, the seminal work (Schick and Schütze, 2020a) proposed a model called PET, which uses a pre-defined set of prompts to perform various NLP tasks as text classification. They also impose a choice of a verbalizer which highly impacts the classification performances (Cui et al., 2022; Hu et al., 2021a). However, recent studies have questioned the benefits of prompt-based learning due to the high variability in performance caused by the choice of prompt (Liu et al., 2022). To address this issue, researchers have proposed prompt tuning which involves a few learnable parameters in addition to the prompt (Lester et al., 2021). Nevertheless, these approaches face limitations when learning from API: (i) encoder access for gradient computation is infeasible (as in R1), (ii) prompting requires to send data and label which raises privacy concerns (as in R3), and (iii) labeling new points is time-consuming (see in R3) and expensive due to the need to send all shots for each input token[1]. These methods, such as adapters (Houlsby et al., 2019; Pfeiffer et al., 2020), keep most of the model’s parameters fixed during training and only update small feedforward networks that are inserted within the larger model architecture. A recent example is T-FEW (Liu et al., 2022), which adds learned vectors that rescale the network’s internal activations. Additionally, it requires a set of manually created prompts for each dataset making it hard to use in practice. Relying on parameter-efficient fine-tuning methods with an API is not possible due to the need to compute gradients of the encoder (as per R1) and the requirement to send both the labeling schema and the labels, which violates R3. Parameter-efficient fine-tuning. In-context learning models are models that utilize input-to-output training examples as prompts to make predictions, without any parameter updates (Wei et al., 2022). These models, such as text-davinci, rely solely on the provided examples to generate predictions, without any additional training. However, a significant drawback of this approach is that the user must supply the input, label examples, and task description, which becomes prohibitively expensive when the number of classes or shots increases, is slow (Liu et al., 2022) (R2) and raises data privacy concerns (as highlighted in R3). Additionally, the inability to reuse text embeddings for new tasks or with new labels without querying the model’s API limits practicality and scalability, making reusable encoding unfeasible for in-context learning models [2]. In Context Learning (ICL). or quite long stood as the de-facto paradigm for FSL ((Snell et al., 2017; Rusu et al., 2019; Sung et al., 2018b; Lee et al., 2019; Raghu et al., 2019; Sun et al., 2019a)). In meta-learning, the objective is to provide the model with the intrinsic ability to learn in a data-efficient manner. For instance, MAML ((Finn et al., 2017a; Antoniou et al., 2018)), arguably the most popular meta-learning method, tries to train a model such that it can be fine-tuned end-to-end using only a few supervised samples while retaining high generalization ability. Unlike the three previous lines of work, meta-learning methods operate by modifying the pre-training procedure and therefore assume access to both the training data and the model, which wholly breaks both R1 and R3. 2.2 Inductive vs transductive learning Learning an inductive classifier on embeddings generated by an API-based model, as proposed by (Snell et al., 2017), is a common baseline for performing FSL. This approach is prevalent in NLP, where a parametric model is trained on data to infer general rules that are applied to label new, unseen data (known as inductive learning (Vapnik, 1999)). However, in FSL scenarios with limited labeled data, this approach can be highly ambiguous and lead to poor generalization. Transduction offers an attractive alternative to inductive learning (Sain, 1996). Unlike inductive learning, which infers general rules from training data, transduction involves finding rules that work specifically for the unlabeled test data. By utilizing more data, such as unlabeled test instances, and aiming for a more localized rule rather than a general one, transductive learning has shown promise and practical benefits in computer vision (Boudiaf et al., 2020, 2021; Ziko et al., 2020). Transductive methods yield substantially better performance than their inductive counterparts by leveraging the statistics of the query set (Dhillon et al., 2019). However, this approach has not yet been explored in the context of textual data. [1] The cost of API queries is determined by the number of input tokens that are transmitted. [2] Furthermore, as the number of considered classes increases, the fixed size of the transformer limits the number of possible shots that can be fed to the model. Previous studies have often neglected this limitation by focusing on a few numbers of labels.

Transductive Learning for Textual Few-Shot Classification: Related Work

Have you ever wondered how your face would look as a whimsical cartoon or how you could magically change your hair color in a photograph? The cutting-edge technology we're about to explore can make those dreams a reality and take your images to dazzling new heights! 🎨🚀 This most recent video is about a revolutionary AI architecture known as StyleGANEX. Developed by brilliant researchers and showcased at ICCV 2023, StyleGANEX is all about pushing boundaries and shattering limitations. No more settling for pixelated or low-res images! StyleGANEX elegantly tackles the challenges of flexible input sizes, allowing you to transform any image into a stunning work of art without compromising on quality. 🌈 And the best thing about it is that their code is fully open-source, and they have a demo available online (all links in the video description)! Obviously, the architecture is mostly based on StyleGAN, which has lots of pre-trained models you can play with, but all have this input image size issue we discussed, which ends up producing different encodings in the latent space too since they all follow the same convolutions. So, everything is of different sizes based on the image you want to process. Here, the researchers did a simple fix to this issue by implementing a dilated convolution to rescale the receptive fields of shallow layers in the StyleGAN generator architecture. But what does this mean? I'll guide you through the power of dilated convolutions, the wizardry that makes StyleGANEX an absolute game-changer in the world of AI image editing. And the best part? You don't need to be a tech expert to understand it all! 🧙🎓 Don't miss out – hit that play button and immerse yourself in the magic of StyleGANEX. And hey, if you're attending ICCV 2023 in Paris, we might cross paths! 🗼 https://youtu.be/Xj20I2zFYi0?embedable=true References: ►Read the full article: https://www.louisbouchard.ai/styleganex/ ►Demo: https://huggingface.co/spaces/PKUWilliamYang/StyleGANEX ►Paper: https://arxiv.org/abs/2303.06146 ►Code: https://github.com/williamyang1991/StyleGANEX ►Project page: https://www.mmlab-ntu.com/project/styleganex/ ►Twitter: https://twitter.com/Whats_AI ►My Newsletter (A new AI application explained weekly to your emails!): https://www.louisbouchard.ai/newsletter/ ►Support me on Patreon: https://www.patreon.com/whatsai ►Join Our AI Discord: https://discord.gg/learnaitogether

AI Image Magic: Creating HD Photos and Funny Cartoons With StyleGANEX

Storybook V2 Comic Book Builder: Part 2, The Document Generator

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