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cid string | eid int64 | element string | type string | rq string | row int64 | hop int64 | rel string | seeds list | sim float64 | maxsim_all float64 | title string | year int64 | abstract string |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
B2022-s1-4350|W4281807880 | 1 | emojis | setting | How can we systematically quantify the emotional content of emojis across multiple dimensions to enable comprehensive analysis of digital communication? | 2,061,326 | 0 | seed | [
0
] | 0.595 | 0.772 | Emoji and rapport management in Spanish WhatsApp chats | 2,019 | |
B2022-s1-4350|W4281807880 | 1 | emojis | setting | How can we systematically quantify the emotional content of emojis across multiple dimensions to enable comprehensive analysis of digital communication? | 1,035,589 | 1 | ref | [
0
] | 0.772 | 0.772 | Jenseits des Alphabets – Kommunikation mit Emojis | 2,017 | Abstract The paper provides an overview of the functions of emojis in everyday written communication – either used to complement or to replace text. The first chapter presents the current research literature on this topic and addresses the differences between unicode emojis and the former ASCII-signs. Then we discuss a... |
B2022-s1-4350|W4281807880 | 1 | emojis | setting | How can we systematically quantify the emotional content of emojis across multiple dimensions to enable comprehensive analysis of digital communication? | 729,135 | 1 | ref | [
0
] | 0.763 | 0.772 | Emoticonos y emojis: análisis de su historia, difusión y uso en la comunicación digital actual | 2,016 | |
B2022-s1-4350|W4281807880 | 1 | emojis | setting | How can we systematically quantify the emotional content of emojis across multiple dimensions to enable comprehensive analysis of digital communication? | 2,057,080 | 1 | citer | [
0
] | 0.759 | 0.772 | A Systematic Review of Emoji: Current Research and Future Perspectives | 2,019 | A growing body of research explores emoji, which are visual symbols in computer mediated communication (CMC). In the 20 years since the first set of emoji was released, research on it has been on the increase, albeit in a variety of directions. We reviewed the extant body of research on emoji and noted the development,... |
B2022-s1-4350|W4281807880 | 4 | digital communication | setting | How can we systematically quantify the emotional content of emojis across multiple dimensions to enable comprehensive analysis of digital communication? | 2,061,326 | 0 | seed | [
0
] | 0.48 | 0.767 | Emoji and rapport management in Spanish WhatsApp chats | 2,019 | Abstract Emoji are a set of pictographs available on several electronic platforms and applications, which are gradually replacing emoticons (sequences of punctuation marks representing facial expressions). Over the last decade, researchers have proposed that emoticons not only convey emotional content in computer-media... |
B2022-s1-4350|W4281807880 | 4 | digital communication | setting | How can we systematically quantify the emotional content of emojis across multiple dimensions to enable comprehensive analysis of digital communication? | 1,553,925 | 1 | ref | [
0
] | 0.767 | 0.767 | Exploring Chinese digital communication | 2,018 | |
B2022-s1-4350|W4281807880 | 4 | digital communication | setting | How can we systematically quantify the emotional content of emojis across multiple dimensions to enable comprehensive analysis of digital communication? | 1,035,589 | 1 | ref | [
0
] | 0.669 | 0.767 | Jenseits des Alphabets – Kommunikation mit Emojis | 2,017 | Abstract The paper provides an overview of the functions of emojis in everyday written communication – either used to complement or to replace text. The first chapter presents the current research literature on this topic and addresses the differences between unicode emojis and the former ASCII-signs. Then we discuss a... |
B2022-s1-4350|W4281807880 | 4 | digital communication | setting | How can we systematically quantify the emotional content of emojis across multiple dimensions to enable comprehensive analysis of digital communication? | 2,910,160 | 1 | ref | [
0
] | 0.667 | 0.767 | Analyzing digital discourse: new insights and future directions | 2,020 | We are living in a digital world where we communicate through the computer-mediated discourse (also known as the digital discourse) more and more frequently. It is significant theoretically and pra... |
B2022-s1-4350|W4206622114 | 0 | perceived adequacy of emoji use | objective | How does the perceived adequacy of emoji use in text-based messages vary depending on the relationship proximity between sender and receiver and the valence of the message? | 2,061,326 | 0 | seed | [
0
] | 0.614 | 0.73 | Emoji and rapport management in Spanish WhatsApp chats | 2,019 | Abstract Emoji are a set of pictographs available on several electronic platforms and applications, which are gradually replacing emoticons (sequences of punctuation marks representing facial expressions). Over the last decade, researchers have proposed that emoticons not only convey emotional content in computer-media... |
B2022-s1-4350|W4206622114 | 0 | perceived adequacy of emoji use | objective | How does the perceived adequacy of emoji use in text-based messages vary depending on the relationship proximity between sender and receiver and the valence of the message? | 2,652,628 | 1 | citer | [
0
] | 0.73 | 0.73 | Use and Interpretation of Emoji in Electronic-Mediated Communication: A Survey | 2,020 | Among the pictorial representations that have succeeded in electronic-mediated communication (EMC), the rise of emoji is of special interest, as they have widely spread around the world. This article reports on the results of a survey conducted among students and staff of a large Spanish university (63% women) on the u... |
B2022-s1-4350|W4206622114 | 0 | perceived adequacy of emoji use | objective | How does the perceived adequacy of emoji use in text-based messages vary depending on the relationship proximity between sender and receiver and the valence of the message? | 1,525,781 | 1 | ref | [
0
] | 0.728 | 0.73 | Motives, frequency and attitudes toward emoji and emoticon use | 2,018 | Abstract Electronic Mediated Communication (EMC) has become highly prevalent in our daily lives. Many of the communication formats used in EMC are text-based (e.g., instant messaging), and users often include visual paralinguistic cues in their messages. In the current study, we examined the usage of two such cues – em... |
B2022-s1-4350|W4206622114 | 0 | perceived adequacy of emoji use | objective | How does the perceived adequacy of emoji use in text-based messages vary depending on the relationship proximity between sender and receiver and the valence of the message? | 1,029,695 | 1 | ref | [
0
] | 0.699 | 0.73 | Assessing personality using emoji: An exploratory study | 2,017 | |
B2022-s1-4350|W4206622114 | 1 | text-based messages | setting | How does the perceived adequacy of emoji use in text-based messages vary depending on the relationship proximity between sender and receiver and the valence of the message? | 2,061,326 | 0 | seed | [
0
] | 0.535 | 0.668 | Emoji and rapport management in Spanish WhatsApp chats | 2,019 | |
B2022-s1-4350|W4206622114 | 1 | text-based messages | setting | How does the perceived adequacy of emoji use in text-based messages vary depending on the relationship proximity between sender and receiver and the valence of the message? | 1,025,175 | 1 | ref | [
0
] | 0.668 | 0.668 | Emojis as Tools for Emotion Work: Communicating Affect in Text Messages | 2,017 | |
B2022-s1-4350|W4206622114 | 1 | text-based messages | setting | How does the perceived adequacy of emoji use in text-based messages vary depending on the relationship proximity between sender and receiver and the valence of the message? | 1,035,589 | 1 | ref | [
0
] | 0.639 | 0.668 | Jenseits des Alphabets – Kommunikation mit Emojis | 2,017 | Abstract The paper provides an overview of the functions of emojis in everyday written communication – either used to complement or to replace text. The first chapter presents the current research literature on this topic and addresses the differences between unicode emojis and the former ASCII-signs. Then we discuss a... |
B2022-s1-4350|W4206622114 | 1 | text-based messages | setting | How does the perceived adequacy of emoji use in text-based messages vary depending on the relationship proximity between sender and receiver and the valence of the message? | 20,196 | 1 | ref | [
0
] | 0.594 | 0.668 | ANÁLISIS LEXICOMÉTRICO DE LA ESPECIFICIDAD DE LA ESCRITURA DIGITAL DEL ADOLESCENTE EN WHATSAPP | 2,015 | |
B2022-s1-4350|W4206622114 | 2 | relationship proximity | concept | How does the perceived adequacy of emoji use in text-based messages vary depending on the relationship proximity between sender and receiver and the valence of the message? | 2,061,326 | 0 | seed | [
0
] | 0.424 | 0.496 | Emoji and rapport management in Spanish WhatsApp chats | 2,019 | Abstract Emoji are a set of pictographs available on several electronic platforms and applications, which are gradually replacing emoticons (sequences of punctuation marks representing facial expressions). Over the last decade, researchers have proposed that emoticons not only convey emotional content in computer-media... |
B2022-s1-4350|W4206622114 | 2 | relationship proximity | concept | How does the perceived adequacy of emoji use in text-based messages vary depending on the relationship proximity between sender and receiver and the valence of the message? | 2,020,482 | 1 | citer | [
0
] | 0.496 | 0.496 | 2,018 | With the rapid development of social media, emojis have gradually become an indispensable communication tool in computer-mediated communication (CMC).In recent years, researchers have found that emojis not only have semantic and syntax functions (e.g., punctuation), but also perform pragmatic functions.Therefore, this ... | |
B2022-s1-4350|W4206622114 | 2 | relationship proximity | concept | How does the perceived adequacy of emoji use in text-based messages vary depending on the relationship proximity between sender and receiver and the valence of the message? | 1,550,361 | 1 | ref | [
0
] | 0.422 | 0.496 | Emoticons in Relational Writing Practices on WhatsApp: Some Reflections on Gender | 2,018 | |
B2022-s1-4350|W4206622114 | 2 | relationship proximity | concept | How does the perceived adequacy of emoji use in text-based messages vary depending on the relationship proximity between sender and receiver and the valence of the message? | 1,603 | 1 | ref | [
0
] | 0.422 | 0.496 | The Conservatism of Emoji: Work, Affect, and Communication | 2,015 | This piece examines emoji as conduits for affective labor in the social networks of informational capitalism. Emoji, ubiquitous digital images that can appear in text messages, emails, and social media chat platforms, are rich in social, cultural, and economic significance. This article examines emoji as historical, so... |
B2022-s1-4350|W4206622114 | 3 | sender and receiver | concept | How does the perceived adequacy of emoji use in text-based messages vary depending on the relationship proximity between sender and receiver and the valence of the message? | 2,061,326 | 0 | seed | [
0
] | 0.472 | 0.529 | Emoji and rapport management in Spanish WhatsApp chats | 2,019 | Abstract Emoji are a set of pictographs available on several electronic platforms and applications, which are gradually replacing emoticons (sequences of punctuation marks representing facial expressions). Over the last decade, researchers have proposed that emoticons not only convey emotional content in computer-media... |
B2022-s1-4350|W4206622114 | 3 | sender and receiver | concept | How does the perceived adequacy of emoji use in text-based messages vary depending on the relationship proximity between sender and receiver and the valence of the message? | 1,603 | 1 | ref | [
0
] | 0.529 | 0.529 | The Conservatism of Emoji: Work, Affect, and Communication | 2,015 | This piece examines emoji as conduits for affective labor in the social networks of informational capitalism. Emoji, ubiquitous digital images that can appear in text messages, emails, and social media chat platforms, are rich in social, cultural, and economic significance. This article examines emoji as historical, so... |
B2022-s1-4350|W4206622114 | 3 | sender and receiver | concept | How does the perceived adequacy of emoji use in text-based messages vary depending on the relationship proximity between sender and receiver and the valence of the message? | 506,133 | 1 | ref | [
0
] | 0.524 | 0.529 | Sender-intended functions of emojis in US messaging | 2,016 | Emojis are an extremely common occurrence in mobile communications, but their meaning is open to interpretation. We investigate motivations for their usage in mobile messaging in the US. This study asked 228 participants for the last time that they used one or more emojis in a conversational message, and collected that... |
B2022-s1-4350|W4206622114 | 3 | sender and receiver | concept | How does the perceived adequacy of emoji use in text-based messages vary depending on the relationship proximity between sender and receiver and the valence of the message? | 1,035,589 | 1 | ref | [
0
] | 0.522 | 0.529 | Jenseits des Alphabets – Kommunikation mit Emojis | 2,017 | Abstract The paper provides an overview of the functions of emojis in everyday written communication – either used to complement or to replace text. The first chapter presents the current research literature on this topic and addresses the differences between unicode emojis and the former ASCII-signs. Then we discuss a... |
B2022-s1-4350|W4206622114 | 4 | valence of the message | concept | How does the perceived adequacy of emoji use in text-based messages vary depending on the relationship proximity between sender and receiver and the valence of the message? | 2,061,326 | 0 | seed | [
0
] | 0.48 | 0.572 | Emoji and rapport management in Spanish WhatsApp chats | 2,019 | Abstract Emoji are a set of pictographs available on several electronic platforms and applications, which are gradually replacing emoticons (sequences of punctuation marks representing facial expressions). Over the last decade, researchers have proposed that emoticons not only convey emotional content in computer-media... |
B2022-s1-4350|W4206622114 | 4 | valence of the message | concept | How does the perceived adequacy of emoji use in text-based messages vary depending on the relationship proximity between sender and receiver and the valence of the message? | 1,025,175 | 1 | ref | [
0
] | 0.572 | 0.572 | Emojis as Tools for Emotion Work: Communicating Affect in Text Messages | 2,017 | Emojis are pictures commonly used in texting. The use and type of emojis has increased in recent years; particularly emojis that are not faces, but rather objects. While prior work on emojis of faces suggest their primary purpose is to convey affect, few have researched the communicative purpose of emojis of objects. I... |
B2022-s1-4350|W4206622114 | 4 | valence of the message | concept | How does the perceived adequacy of emoji use in text-based messages vary depending on the relationship proximity between sender and receiver and the valence of the message? | 1,603 | 1 | ref | [
0
] | 0.565 | 0.572 | The Conservatism of Emoji: Work, Affect, and Communication | 2,015 | This piece examines emoji as conduits for affective labor in the social networks of informational capitalism. Emoji, ubiquitous digital images that can appear in text messages, emails, and social media chat platforms, are rich in social, cultural, and economic significance. This article examines emoji as historical, so... |
B2022-s1-4350|W4206622114 | 4 | valence of the message | concept | How does the perceived adequacy of emoji use in text-based messages vary depending on the relationship proximity between sender and receiver and the valence of the message? | 506,133 | 1 | ref | [
0
] | 0.543 | 0.572 | Sender-intended functions of emojis in US messaging | 2,016 | Emojis are an extremely common occurrence in mobile communications, but their meaning is open to interpretation. We investigate motivations for their usage in mobile messaging in the US. This study asked 228 participants for the last time that they used one or more emojis in a conversational message, and collected that... |
B2022-s1-4350|W4210846473 | 0 | different emoji | concept | Do different emoji used in first-time online interactions signal distinct relationship initiation intentions to strangers? | 2,061,326 | 0 | seed | [
0
] | 0.609 | 0.768 | Emoji and rapport management in Spanish WhatsApp chats | 2,019 | Abstract Emoji are a set of pictographs available on several electronic platforms and applications, which are gradually replacing emoticons (sequences of punctuation marks representing facial expressions). Over the last decade, researchers have proposed that emoticons not only convey emotional content in computer-media... |
B2022-s1-4350|W4210846473 | 0 | different emoji | concept | Do different emoji used in first-time online interactions signal distinct relationship initiation intentions to strangers? | 3,219,813 | 1 | citer | [
0
] | 0.768 | 0.768 | Digitally saving face: An experimental investigation of cross-cultural differences in the use of emoticons and emoji | 2,021 | |
B2022-s1-4350|W4210846473 | 0 | different emoji | concept | Do different emoji used in first-time online interactions signal distinct relationship initiation intentions to strangers? | 1,029,695 | 1 | ref | [
0
] | 0.765 | 0.768 | Assessing personality using emoji: An exploratory study | 2,017 | |
B2022-s1-4350|W4210846473 | 0 | different emoji | concept | Do different emoji used in first-time online interactions signal distinct relationship initiation intentions to strangers? | 1,035,589 | 1 | ref | [
0
] | 0.747 | 0.768 | Jenseits des Alphabets – Kommunikation mit Emojis | 2,017 | Abstract The paper provides an overview of the functions of emojis in everyday written communication – either used to complement or to replace text. The first chapter presents the current research literature on this topic and addresses the differences between unicode emojis and the former ASCII-signs. Then we discuss a... |
B2022-s1-4350|W4210846473 | 1 | first-time online interactions | setting | Do different emoji used in first-time online interactions signal distinct relationship initiation intentions to strangers? | 2,061,326 | 0 | seed | [
0
] | 0.448 | 0.557 | Emoji and rapport management in Spanish WhatsApp chats | 2,019 | Abstract Emoji are a set of pictographs available on several electronic platforms and applications, which are gradually replacing emoticons (sequences of punctuation marks representing facial expressions). Over the last decade, researchers have proposed that emoticons not only convey emotional content in computer-media... |
B2022-s1-4350|W4210846473 | 1 | first-time online interactions | setting | Do different emoji used in first-time online interactions signal distinct relationship initiation intentions to strangers? | 2,658,727 | 1 | citer | [
0
] | 0.557 | 0.557 | Testing, filtering, and insinuating: Matching and attunement of emoji use patterns as non-verbal flirting in online dating | 2,020 | Abstract This study adds to research on digital interactions by exploring the role of emoji use patterns in online dating among young adults. Focus group data suggest emojis play a similar role as non-verbal behaviors do in an offline context, where coordination is used to ascertain mutual interest. The data shows how ... |
B2022-s1-4350|W4210846473 | 1 | first-time online interactions | setting | Do different emoji used in first-time online interactions signal distinct relationship initiation intentions to strangers? | 2,750,657 | 1 | citer | [
0
] | 0.555 | 0.557 | Hábitos informativos y participación online: un estudio sobre los y las jóvenes universitarios/as en Argentina | 2,020 | Esta investigación explora los hábitos informativos y las prácticas de participación digital de los y las jóvenes universitarios/as en Argentina. Se utilizó una metodología mixta que combinó datos cuantitativos y cualitativos obtenidos a partir de encuestas (n=1243) y entrevistas (n=132). Los resultados indican que los... |
B2022-s1-4350|W4210846473 | 1 | first-time online interactions | setting | Do different emoji used in first-time online interactions signal distinct relationship initiation intentions to strangers? | 2,584,749 | 1 | citer | [
0
] | 0.537 | 0.557 | ผลกระทบของคณลกษณะแชทบอทตอการรบรของผเยยมชมโซเชยลคอมเมรซ | 2,019 | ด้วยการเติบโตของแชทบอทในธุรกิจโซเชียลคอมเมิร์ซ และความสามารถของหลักภาษาไทย งานวิจัยนี้จึงมีจุดมุ่งหมายในการวิเคราะห์และเปรียบเทียบผลกระทบของคุณลักษณะของแชทบอท ได้แก่ การรับรู้เพศ และการรับรู้ความเป็นทางการ ที่มีต่อความพึงพอใจและการรับรู้คุณภาพบริการโดยใช้แบบจำลอง SERVQUAL ในขณะที่ผู้เยี่ยมชมใช้แชทบอทเลือกซื้อสินค้าผ่าน... |
B2022-s1-4350|W4210846473 | 2 | relationship initiation intentions | concept | Do different emoji used in first-time online interactions signal distinct relationship initiation intentions to strangers? | 2,061,326 | 0 | seed | [
0
] | 0.429 | 0.542 | Emoji and rapport management in Spanish WhatsApp chats | 2,019 | Abstract Emoji are a set of pictographs available on several electronic platforms and applications, which are gradually replacing emoticons (sequences of punctuation marks representing facial expressions). Over the last decade, researchers have proposed that emoticons not only convey emotional content in computer-media... |
B2022-s1-4350|W4210846473 | 2 | relationship initiation intentions | concept | Do different emoji used in first-time online interactions signal distinct relationship initiation intentions to strangers? | 1,525,781 | 1 | ref | [
0
] | 0.542 | 0.542 | Motives, frequency and attitudes toward emoji and emoticon use | 2,018 | Abstract Electronic Mediated Communication (EMC) has become highly prevalent in our daily lives. Many of the communication formats used in EMC are text-based (e.g., instant messaging), and users often include visual paralinguistic cues in their messages. In the current study, we examined the usage of two such cues – em... |
B2022-s1-4350|W4210846473 | 2 | relationship initiation intentions | concept | Do different emoji used in first-time online interactions signal distinct relationship initiation intentions to strangers? | 2,658,727 | 1 | citer | [
0
] | 0.531 | 0.542 | Testing, filtering, and insinuating: Matching and attunement of emoji use patterns as non-verbal flirting in online dating | 2,020 | Abstract This study adds to research on digital interactions by exploring the role of emoji use patterns in online dating among young adults. Focus group data suggest emojis play a similar role as non-verbal behaviors do in an offline context, where coordination is used to ascertain mutual interest. The data shows how ... |
B2022-s1-4350|W4210846473 | 2 | relationship initiation intentions | concept | Do different emoji used in first-time online interactions signal distinct relationship initiation intentions to strangers? | 506,133 | 1 | ref | [
0
] | 0.508 | 0.542 | Sender-intended functions of emojis in US messaging | 2,016 | Emojis are an extremely common occurrence in mobile communications, but their meaning is open to interpretation. We investigate motivations for their usage in mobile messaging in the US. This study asked 228 participants for the last time that they used one or more emojis in a conversational message, and collected that... |
B2022-s1-4350|W4210846473 | 3 | strangers | setting | Do different emoji used in first-time online interactions signal distinct relationship initiation intentions to strangers? | 2,061,326 | 0 | seed | [
0
] | 0.429 | 0.526 | Emoji and rapport management in Spanish WhatsApp chats | 2,019 | Abstract Emoji are a set of pictographs available on several electronic platforms and applications, which are gradually replacing emoticons (sequences of punctuation marks representing facial expressions). Over the last decade, researchers have proposed that emoticons not only convey emotional content in computer-media... |
B2022-s1-4350|W4210846473 | 3 | strangers | setting | Do different emoji used in first-time online interactions signal distinct relationship initiation intentions to strangers? | 2,020,482 | 1 | citer | [
0
] | 0.526 | 0.526 | 2,018 | With the rapid development of social media, emojis have gradually become an indispensable communication tool in computer-mediated communication (CMC).In recent years, researchers have found that emojis not only have semantic and syntax functions (e.g., punctuation), but also perform pragmatic functions.Therefore, this ... | |
B2022-s1-4350|W4210846473 | 3 | strangers | setting | Do different emoji used in first-time online interactions signal distinct relationship initiation intentions to strangers? | 1,035,589 | 1 | ref | [
0
] | 0.481 | 0.526 | Jenseits des Alphabets – Kommunikation mit Emojis | 2,017 | Abstract The paper provides an overview of the functions of emojis in everyday written communication – either used to complement or to replace text. The first chapter presents the current research literature on this topic and addresses the differences between unicode emojis and the former ASCII-signs. Then we discuss a... |
B2022-s1-4350|W4210846473 | 3 | strangers | setting | Do different emoji used in first-time online interactions signal distinct relationship initiation intentions to strangers? | 1,029,695 | 1 | ref | [
0
] | 0.476 | 0.526 | Assessing personality using emoji: An exploratory study | 2,017 | |
B2022-r10-1201|W4313118910 | 0 | adversarial training | method | Can adversarial training effectively protect ML-based modulation classifiers against physical eavesdropping evasion attacks in wireless communication systems? | 1,256,595 | 0 | seed | [
2
] | 0.819 | 0.86 | Ensemble Adversarial Training: Attacks and Defenses | 2,017 | |
B2022-r10-1201|W4313118910 | 0 | adversarial training | method | Can adversarial training effectively protect ML-based modulation classifiers against physical eavesdropping evasion attacks in wireless communication systems? | 1,256,566 | 0 | seed | [
4
] | 0.798 | 0.86 | Towards Deep Learning Models Resistant to Adversarial Attacks | 2,017 | Recent work has demonstrated that deep neural networks are vulnerable to adversarial examples---inputs that are almost indistinguishable from natural data and yet classified incorrectly by the network. In fact, some of the latest findings suggest that the existence of adversarial attacks may be an inherent weakness of ... |
B2022-r10-1201|W4313118910 | 0 | adversarial training | method | Can adversarial training effectively protect ML-based modulation classifiers against physical eavesdropping evasion attacks in wireless communication systems? | 3,198,688 | 1 | citer | [
2,
4
] | 0.86 | 0.86 | Recent Advances in Adversarial Training for Adversarial Robustness | 2,021 | Adversarial training is one of the most effective approaches for deep learning models to defend against adversarial examples. Unlike other defense strategies, adversarial training aims to enhance the robustness of models intrinsically. During the past few years, adversarial training has been studied and discussed from ... |
B2022-r10-1201|W4313118910 | 0 | adversarial training | method | Can adversarial training effectively protect ML-based modulation classifiers against physical eavesdropping evasion attacks in wireless communication systems? | 2,908,704 | 1 | citer | [
1
] | 0.854 | 0.86 | Bag of Tricks for Adversarial Training | 2,020 | Adversarial training (AT) is one of the most effective strategies for promoting model robustness. However, recent benchmarks show that most of the proposed improvements on AT are less effective than simply early stopping the training procedure. This counter-intuitive fact motivates us to investigate the implementation ... |
B2022-r10-1201|W4313118910 | 0 | adversarial training | method | Can adversarial training effectively protect ML-based modulation classifiers against physical eavesdropping evasion attacks in wireless communication systems? | 3,507,239 | 1 | citer | [
0,
2
] | 0.852 | 0.86 | On the Convergence and Robustness of Adversarial Training | 2,021 | Improving the robustness of deep neural networks (DNNs) to adversarial examples is an important yet challenging problem for secure deep learning. Across existing defense techniques, adversarial training with Projected Gradient Decent (PGD) is amongst the most effective. Adversarial training solves a min-max optimizatio... |
B2022-r10-1201|W4313118910 | 0 | adversarial training | method | Can adversarial training effectively protect ML-based modulation classifiers against physical eavesdropping evasion attacks in wireless communication systems? | 2,334,140 | 2 | cocited | [] | 0.679 | 0.86 | Certified Adversarial Robustness via Randomized Smoothing | 2,019 | We show how to turn any classifier that classifies well under Gaussian noise into a new classifier that is certifiably robust to adversarial perturbations under the $\ell_2$ norm. This "randomized smoothing" technique has been proposed recently in the literature, but existing guarantees are loose. We prove a tight robu... |
B2022-r10-1201|W4313118910 | 0 | adversarial training | method | Can adversarial training effectively protect ML-based modulation classifiers against physical eavesdropping evasion attacks in wireless communication systems? | 1,523,544 | 2 | cocited | [] | 0.673 | 0.86 | Synthesizing Robust Adversarial Examples | 2,018 | Standard methods for generating adversarial examples for neural networks do not consistently fool neural network classifiers in the physical world due to a combination of viewpoint shifts, camera noise, and other natural transformations, limiting their relevance to real-world systems. We demonstrate the existence of ro... |
B2022-r10-1201|W4313118910 | 2 | ML-based modulation classifiers | concept | Can adversarial training effectively protect ML-based modulation classifiers against physical eavesdropping evasion attacks in wireless communication systems? | 1,256,680 | 0 | seed | [
1
] | 0.456 | 0.573 | On Detecting Adversarial Perturbations | 2,017 | Machine learning and deep learning in particular has advanced tremendously on perceptual tasks in recent years. However, it remains vulnerable against adversarial perturbations of the input that have been crafted specifically to fool the system while being quasi-imperceptible to a human. In this work, we propose to aug... |
B2022-r10-1201|W4313118910 | 2 | ML-based modulation classifiers | concept | Can adversarial training effectively protect ML-based modulation classifiers against physical eavesdropping evasion attacks in wireless communication systems? | 1,256,566 | 0 | seed | [
4
] | 0.433 | 0.573 | Towards Deep Learning Models Resistant to Adversarial Attacks | 2,017 | Recent work has demonstrated that deep neural networks are vulnerable to adversarial examples---inputs that are almost indistinguishable from natural data and yet classified incorrectly by the network. In fact, some of the latest findings suggest that the existence of adversarial attacks may be an inherent weakness of ... |
B2022-r10-1201|W4313118910 | 2 | ML-based modulation classifiers | concept | Can adversarial training effectively protect ML-based modulation classifiers against physical eavesdropping evasion attacks in wireless communication systems? | 2,618,077 | 1 | citer | [
4
] | 0.573 | 0.573 | Adversarial Attacks in Modulation Recognition With Convolutional Neural Networks | 2,020 | Deep learning (DL) models are vulnerable to adversarial attacks, by adding a subtle perturbation which is imperceptible to the human eye, a convolutional neural network (CNN) can lead to erroneous results, which greatly reduces the reliability and security of the DL tasks. Considering the wide application of modulation... |
B2022-r10-1201|W4313118910 | 2 | ML-based modulation classifiers | concept | Can adversarial training effectively protect ML-based modulation classifiers against physical eavesdropping evasion attacks in wireless communication systems? | 2,072,307 | 1 | citer | [
3
] | 0.55 | 0.573 | Adversarial Examples in RF Deep Learning: Detection and Physical Robustness | 2,019 | While research on adversarial examples (AdExs) in machine learning for images has been prolific, similar attacks on deep learning (DL) for radio frequency (RF) signals and corresponding mitigation strategies are scarcely addressed in the published work, with only a handful of recent publications in the RF domain. With ... |
B2022-r10-1201|W4313118910 | 2 | ML-based modulation classifiers | concept | Can adversarial training effectively protect ML-based modulation classifiers against physical eavesdropping evasion attacks in wireless communication systems? | 2,334,523 | 1 | citer | [
0
] | 0.536 | 0.573 | Your Classifier is Secretly an Energy Based Model and You Should Treat it Like One | 2,019 | We propose to reinterpret a standard discriminative classifier of p(y|x) as an energy based model for the joint distribution p(x,y). In this setting, the standard class probabilities can be easily computed as well as unnormalized values of p(x) and p(x|y). Within this framework, standard discriminative architectures ma... |
B2022-r10-1201|W4313118910 | 2 | ML-based modulation classifiers | concept | Can adversarial training effectively protect ML-based modulation classifiers against physical eavesdropping evasion attacks in wireless communication systems? | 218,343 | 2 | cocited | [] | 0.433 | 0.573 | Distilling the Knowledge in a Neural Network | 2,015 | A very simple way to improve the performance of almost any machine learning algorithm is to train many different models on the same data and then to average their predictions. Unfortunately, making predictions using a whole ensemble of models is cumbersome and may be too computationally expensive to allow deployment to... |
B2022-r10-1201|W4313118910 | 2 | ML-based modulation classifiers | concept | Can adversarial training effectively protect ML-based modulation classifiers against physical eavesdropping evasion attacks in wireless communication systems? | 1,022,590 | 2 | cocited | [] | 0.424 | 0.573 | Universal Adversarial Perturbations | 2,017 | Given a state-of-the-art deep neural network classifier, we show the existence of a universal (image-agnostic) and very small perturbation vector that causes natural images to be misclassified with high probability. We propose a systematic algorithm for computing universal perturbations, and show that state-of-the-art ... |
B2022-r10-1201|W4313118910 | 3 | physical eavesdropping evasion attacks | problem | Can adversarial training effectively protect ML-based modulation classifiers against physical eavesdropping evasion attacks in wireless communication systems? | 1,779,215 | 0 | seed | [
0
] | 0.55 | 0.627 | Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples | 2,018 | We identify obfuscated gradients, a kind of gradient masking, as a phenomenon that leads to a false sense of security in defenses against adversarial examples. While defenses that cause obfuscated gradients appear to defeat iterative optimization-based attacks, we find defenses relying on this effect can be circumvente... |
B2022-r10-1201|W4313118910 | 3 | physical eavesdropping evasion attacks | problem | Can adversarial training effectively protect ML-based modulation classifiers against physical eavesdropping evasion attacks in wireless communication systems? | 1,779,342 | 0 | seed | [
3
] | 0.514 | 0.627 | Certified Defenses against Adversarial Examples | 2,018 | While neural networks have achieved high accuracy on standard image classification benchmarks, their accuracy drops to nearly zero in the presence of small adversarial perturbations to test inputs. Defenses based on regularization and adversarial training have been proposed, but often followed by new, stronger attacks ... |
B2022-r10-1201|W4313118910 | 3 | physical eavesdropping evasion attacks | problem | Can adversarial training effectively protect ML-based modulation classifiers against physical eavesdropping evasion attacks in wireless communication systems? | 3,200,378 | 1 | citer | [
4
] | 0.627 | 0.627 | Dual Attention Suppression Attack: Generate Adversarial Camouflage in Physical World | 2,021 | Deep learning models are vulnerable to adversarial examples. As a more threatening type for practical deep learning systems, physical adversarial examples have received extensive research attention in recent years. However, without exploiting the intrinsic characteristics such as model-agnostic and human-specific patte... |
B2022-r10-1201|W4313118910 | 3 | physical eavesdropping evasion attacks | problem | Can adversarial training effectively protect ML-based modulation classifiers against physical eavesdropping evasion attacks in wireless communication systems? | 3,200,294 | 1 | citer | [
0
] | 0.625 | 0.627 | Backdoor Attacks Against Deep Learning Systems in the Physical World | 2,021 | Backdoor attacks embed hidden malicious behaviors into deep learning models, which only activate and cause misclassifications on model inputs containing a specific "trigger." Existing works on backdoor attacks and defenses, however, mostly focus on digital attacks that apply digitally generated patterns as triggers. A ... |
B2022-r10-1201|W4313118910 | 3 | physical eavesdropping evasion attacks | problem | Can adversarial training effectively protect ML-based modulation classifiers against physical eavesdropping evasion attacks in wireless communication systems? | 1,257,123 | 1 | ref | [
3
] | 0.613 | 0.627 | Robust Physical-World Attacks on Machine Learning Models. | 2,017 | Deep neural network-based classifiers are known to be vulnerable to adversarial examples that can fool them into misclassifying their input through the addition of small-magnitude perturbations. However, recent studies have demonstrated that such adversarial examples are not very effective in the physical world--they e... |
B2022-r10-1201|W4313118910 | 3 | physical eavesdropping evasion attacks | problem | Can adversarial training effectively protect ML-based modulation classifiers against physical eavesdropping evasion attacks in wireless communication systems? | 1,522,302 | 2 | cocited | [] | 0.535 | 0.627 | Robust Physical-World Attacks on Deep Learning Visual Classification | 2,018 | Recent studies show that the state-of-the-art deep neural networks (DNNs) are vulnerable to adversarial examples, resulting from small-magnitude perturbations added to the input. Given that that emerging physical systems are using DNNs in safety-critical situations, adversarial examples could mislead these systems and ... |
B2022-r10-1201|W4313118910 | 3 | physical eavesdropping evasion attacks | problem | Can adversarial training effectively protect ML-based modulation classifiers against physical eavesdropping evasion attacks in wireless communication systems? | 2,334,140 | 2 | cocited | [] | 0.468 | 0.627 | Certified Adversarial Robustness via Randomized Smoothing | 2,019 | We show how to turn any classifier that classifies well under Gaussian noise into a new classifier that is certifiably robust to adversarial perturbations under the $\ell_2$ norm. This "randomized smoothing" technique has been proposed recently in the literature, but existing guarantees are loose. We prove a tight robu... |
B2022-r10-1201|W4313118910 | 4 | wireless communication systems | setting | Can adversarial training effectively protect ML-based modulation classifiers against physical eavesdropping evasion attacks in wireless communication systems? | 1,256,566 | 0 | seed | [
4
] | 0.393 | 0.576 | Towards Deep Learning Models Resistant to Adversarial Attacks | 2,017 | Recent work has demonstrated that deep neural networks are vulnerable to adversarial examples---inputs that are almost indistinguishable from natural data and yet classified incorrectly by the network. In fact, some of the latest findings suggest that the existence of adversarial attacks may be an inherent weakness of ... |
B2022-r10-1201|W4313118910 | 4 | wireless communication systems | setting | Can adversarial training effectively protect ML-based modulation classifiers against physical eavesdropping evasion attacks in wireless communication systems? | 1,779,342 | 0 | seed | [
3
] | 0.365 | 0.576 | Certified Defenses against Adversarial Examples | 2,018 | While neural networks have achieved high accuracy on standard image classification benchmarks, their accuracy drops to nearly zero in the presence of small adversarial perturbations to test inputs. Defenses based on regularization and adversarial training have been proposed, but often followed by new, stronger attacks ... |
B2022-r10-1201|W4313118910 | 4 | wireless communication systems | setting | Can adversarial training effectively protect ML-based modulation classifiers against physical eavesdropping evasion attacks in wireless communication systems? | 2,621,474 | 1 | citer | [
5
] | 0.576 | 0.576 | Enabling AI in Future Wireless Networks: A Data Life Cycle Perspective | 2,020 | Recent years have seen rapid deployment of mobile computing and Internet of Things (IoT) networks, which can be mostly attributed to the increasing communication and sensing capabilities of wireless systems. Big data analysis, pervasive computing, and eventually artificial intelligence (AI) are envisaged to be deployed... |
B2022-r10-1201|W4313118910 | 4 | wireless communication systems | setting | Can adversarial training effectively protect ML-based modulation classifiers against physical eavesdropping evasion attacks in wireless communication systems? | 3,198,862 | 1 | citer | [
2
] | 0.527 | 0.576 | The Roadmap to 6G Security and Privacy | 2,021 | Although the fifth generation (5G) wireless networks are yet to be fully investigated, the visionaries of the 6th generation (6G) echo systems have already come into the discussion. Therefore, in order to consolidate and solidify the security and privacy in 6G networks, we survey how security may impact the envisioned ... |
B2022-r10-1201|W4313118910 | 4 | wireless communication systems | setting | Can adversarial training effectively protect ML-based modulation classifiers against physical eavesdropping evasion attacks in wireless communication systems? | 3,507,282 | 1 | citer | [
0
] | 0.482 | 0.576 | A Field Guide to Federated Optimization | 2,021 | Federated learning and analytics are a distributed approach for collaboratively learning models (or statistics) from decentralized data, motivated by and designed for privacy protection. The distributed learning process can be formulated as solving federated optimization problems, which emphasize communication efficien... |
B2022-r10-1201|W4313118910 | 4 | wireless communication systems | setting | Can adversarial training effectively protect ML-based modulation classifiers against physical eavesdropping evasion attacks in wireless communication systems? | 1,022,485 | 2 | cocited | [] | 0.447 | 0.576 | Densely Connected Convolutional Networks | 2,017 | Recent work has shown that convolutional networks can be substantially deeper, more accurate, and efficient to train if they contain shorter connections between layers close to the input and those close to the output. In this paper, we embrace this observation and introduce the Dense Convolutional Network (DenseNet), w... |
B2022-r10-1201|W4313118910 | 4 | wireless communication systems | setting | Can adversarial training effectively protect ML-based modulation classifiers against physical eavesdropping evasion attacks in wireless communication systems? | 3 | 2 | cocited | [] | 0.39 | 0.576 | ImageNet Large Scale Visual Recognition Challenge | 2,015 | The ImageNet Large Scale Visual Recognition Challenge is a benchmark in object category classification and detection on hundreds of object categories and millions of images. The challenge has been run annually from 2010 to present, attracting participation from more than fifty institutions. This paper describes the cre... |
B2022-r10-1201|W4200633541 | 0 | multiple models | setting | Can multiple models improve their robustness through collaborative learning by mutually sharing knowledge of adversarial examples, rather than relying on a static teacher model? | 1,256,566 | 0 | seed | [
4
] | 0.54 | 0.589 | Towards Deep Learning Models Resistant to Adversarial Attacks | 2,017 | Recent work has demonstrated that deep neural networks are vulnerable to adversarial examples---inputs that are almost indistinguishable from natural data and yet classified incorrectly by the network. In fact, some of the latest findings suggest that the existence of adversarial attacks may be an inherent weakness of ... |
B2022-r10-1201|W4200633541 | 0 | multiple models | setting | Can multiple models improve their robustness through collaborative learning by mutually sharing knowledge of adversarial examples, rather than relying on a static teacher model? | 1,779,342 | 0 | seed | [
3
] | 0.472 | 0.589 | Certified Defenses against Adversarial Examples | 2,018 | While neural networks have achieved high accuracy on standard image classification benchmarks, their accuracy drops to nearly zero in the presence of small adversarial perturbations to test inputs. Defenses based on regularization and adversarial training have been proposed, but often followed by new, stronger attacks ... |
B2022-r10-1201|W4200633541 | 0 | multiple models | setting | Can multiple models improve their robustness through collaborative learning by mutually sharing knowledge of adversarial examples, rather than relying on a static teacher model? | 3,198,604 | 1 | citer | [
4
] | 0.589 | 0.589 | Deep double descent: where bigger models and more data hurt* | 2,021 | Abstract We show that a variety of modern deep learning tasks exhibit a ‘double-descent’ phenomenon where, as we increase model size, performance first gets worse and then gets better. Moreover, we show that double descent occurs not just as a function of model size, but also as a function of the number of training epo... |
B2022-r10-1201|W4200633541 | 0 | multiple models | setting | Can multiple models improve their robustness through collaborative learning by mutually sharing knowledge of adversarial examples, rather than relying on a static teacher model? | 2,622,299 | 1 | citer | [
1
] | 0.574 | 0.589 | Benchmarking the Robustness of Semantic Segmentation Models | 2,020 | When designing a semantic segmentation module for a practical application, such as autonomous driving, it is crucial to understand the robustness of the module with respect to a wide range of image corruptions. While there are recent robustness studies for full-image classification, we are the first to present an exhau... |
B2022-r10-1201|W4200633541 | 0 | multiple models | setting | Can multiple models improve their robustness through collaborative learning by mutually sharing knowledge of adversarial examples, rather than relying on a static teacher model? | 1,522,589 | 1 | citer | [
2
] | 0.572 | 0.589 | The Case for Learned Index Structures | 2,018 | Indexes are models: a \btree-Index can be seen as a model to map a key to the position of a record within a sorted array, a Hash-Index as a model to map a key to a position of a record within an unsorted array, and a BitMap-Index as a model to indicate if a data record exists or not. In this exploratory research paper,... |
B2022-r10-1201|W4200633541 | 0 | multiple models | setting | Can multiple models improve their robustness through collaborative learning by mutually sharing knowledge of adversarial examples, rather than relying on a static teacher model? | 218,343 | 2 | cocited | [] | 0.51 | 0.589 | Distilling the Knowledge in a Neural Network | 2,015 | A very simple way to improve the performance of almost any machine learning algorithm is to train many different models on the same data and then to average their predictions. Unfortunately, making predictions using a whole ensemble of models is cumbersome and may be too computationally expensive to allow deployment to... |
B2022-r10-1201|W4200633541 | 0 | multiple models | setting | Can multiple models improve their robustness through collaborative learning by mutually sharing knowledge of adversarial examples, rather than relying on a static teacher model? | 1,022,485 | 2 | cocited | [] | 0.503 | 0.589 | Densely Connected Convolutional Networks | 2,017 | Recent work has shown that convolutional networks can be substantially deeper, more accurate, and efficient to train if they contain shorter connections between layers close to the input and those close to the output. In this paper, we embrace this observation and introduce the Dense Convolutional Network (DenseNet), w... |
B2022-r10-1201|W4200633541 | 1 | robustness | objective | Can multiple models improve their robustness through collaborative learning by mutually sharing knowledge of adversarial examples, rather than relying on a static teacher model? | 1,256,566 | 0 | seed | [
4
] | 0.613 | 0.722 | Towards Deep Learning Models Resistant to Adversarial Attacks | 2,017 | Recent work has demonstrated that deep neural networks are vulnerable to adversarial examples---inputs that are almost indistinguishable from natural data and yet classified incorrectly by the network. In fact, some of the latest findings suggest that the existence of adversarial attacks may be an inherent weakness of ... |
B2022-r10-1201|W4200633541 | 1 | robustness | objective | Can multiple models improve their robustness through collaborative learning by mutually sharing knowledge of adversarial examples, rather than relying on a static teacher model? | 1,779,342 | 0 | seed | [
3
] | 0.559 | 0.722 | Certified Defenses against Adversarial Examples | 2,018 | While neural networks have achieved high accuracy on standard image classification benchmarks, their accuracy drops to nearly zero in the presence of small adversarial perturbations to test inputs. Defenses based on regularization and adversarial training have been proposed, but often followed by new, stronger attacks ... |
B2022-r10-1201|W4200633541 | 1 | robustness | objective | Can multiple models improve their robustness through collaborative learning by mutually sharing knowledge of adversarial examples, rather than relying on a static teacher model? | 2,908,481 | 1 | citer | [
0
] | 0.722 | 0.722 | RobustBench: a standardized adversarial robustness benchmark | 2,020 | As a research community, we are still lacking a systematic understanding of the progress on adversarial robustness which often makes it hard to identify the most promising ideas in training robust models. A key challenge in benchmarking robustness is that its evaluation is often error-prone leading to robustness overes... |
B2022-r10-1201|W4200633541 | 1 | robustness | objective | Can multiple models improve their robustness through collaborative learning by mutually sharing knowledge of adversarial examples, rather than relying on a static teacher model? | 3,198,259 | 1 | citer | [
4
] | 0.717 | 0.722 | The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization | 2,021 | We introduce four new real-world distribution shift datasets consisting of changes in image style, image blurriness, geographic location, camera operation, and more. With our new datasets, we take stock of previously proposed methods for improving out-of-distribution robustness and put them to the test. We find that us... |
B2022-r10-1201|W4200633541 | 1 | robustness | objective | Can multiple models improve their robustness through collaborative learning by mutually sharing knowledge of adversarial examples, rather than relying on a static teacher model? | 1,779,258 | 1 | citer | [
0,
3
] | 0.698 | 0.722 | Robustness May Be at Odds with Accuracy | 2,018 | We show that there may exist an inherent tension between the goal of adversarial robustness and that of standard generalization. Specifically, training robust models may not only be more resource-consuming, but also lead to a reduction of standard accuracy. We demonstrate that this trade-off between the standard accura... |
B2022-r10-1201|W4200633541 | 1 | robustness | objective | Can multiple models improve their robustness through collaborative learning by mutually sharing knowledge of adversarial examples, rather than relying on a static teacher model? | 2,334,140 | 2 | cocited | [] | 0.614 | 0.722 | Certified Adversarial Robustness via Randomized Smoothing | 2,019 | We show how to turn any classifier that classifies well under Gaussian noise into a new classifier that is certifiably robust to adversarial perturbations under the $\ell_2$ norm. This "randomized smoothing" technique has been proposed recently in the literature, but existing guarantees are loose. We prove a tight robu... |
B2022-r10-1201|W4200633541 | 1 | robustness | objective | Can multiple models improve their robustness through collaborative learning by mutually sharing knowledge of adversarial examples, rather than relying on a static teacher model? | 1,523,544 | 2 | cocited | [] | 0.57 | 0.722 | Synthesizing Robust Adversarial Examples | 2,018 | Standard methods for generating adversarial examples for neural networks do not consistently fool neural network classifiers in the physical world due to a combination of viewpoint shifts, camera noise, and other natural transformations, limiting their relevance to real-world systems. We demonstrate the existence of ro... |
B2022-r10-1201|W4200633541 | 2 | collaborative learning | method | Can multiple models improve their robustness through collaborative learning by mutually sharing knowledge of adversarial examples, rather than relying on a static teacher model? | 1,256,566 | 0 | seed | [
4
] | 0.408 | 0.529 | Towards Deep Learning Models Resistant to Adversarial Attacks | 2,017 | Recent work has demonstrated that deep neural networks are vulnerable to adversarial examples---inputs that are almost indistinguishable from natural data and yet classified incorrectly by the network. In fact, some of the latest findings suggest that the existence of adversarial attacks may be an inherent weakness of ... |
B2022-r10-1201|W4200633541 | 2 | collaborative learning | method | Can multiple models improve their robustness through collaborative learning by mutually sharing knowledge of adversarial examples, rather than relying on a static teacher model? | 1,779,342 | 0 | seed | [
3
] | 0.375 | 0.529 | Certified Defenses against Adversarial Examples | 2,018 | While neural networks have achieved high accuracy on standard image classification benchmarks, their accuracy drops to nearly zero in the presence of small adversarial perturbations to test inputs. Defenses based on regularization and adversarial training have been proposed, but often followed by new, stronger attacks ... |
B2022-r10-1201|W4200633541 | 2 | collaborative learning | method | Can multiple models improve their robustness through collaborative learning by mutually sharing knowledge of adversarial examples, rather than relying on a static teacher model? | 2,616,026 | 1 | citer | [
0,
2,
4
] | 0.529 | 0.529 | Advances and Open Problems in Federated Learning | 2,020 | Federated learning (FL) is a machine learning setting where many clients (e.g., mobile devices or whole organizations) collaboratively train a model under the orchestration of a central server (e.g., service provider), while keeping the training data decentralized. FL embodies the principles of focused data collection ... |
B2022-r10-1201|W4200633541 | 2 | collaborative learning | method | Can multiple models improve their robustness through collaborative learning by mutually sharing knowledge of adversarial examples, rather than relying on a static teacher model? | 3,507,282 | 1 | citer | [
0
] | 0.502 | 0.529 | A Field Guide to Federated Optimization | 2,021 | Federated learning and analytics are a distributed approach for collaboratively learning models (or statistics) from decentralized data, motivated by and designed for privacy protection. The distributed learning process can be formulated as solving federated optimization problems, which emphasize communication efficien... |
B2022-r10-1201|W4200633541 | 2 | collaborative learning | method | Can multiple models improve their robustness through collaborative learning by mutually sharing knowledge of adversarial examples, rather than relying on a static teacher model? | 2,616,078 | 1 | citer | [
4
] | 0.481 | 0.529 | Self-Training With Noisy Student Improves ImageNet Classification | 2,020 | We present a simple self-training method that achieves 88.4% top-1 accuracy on ImageNet, which is 2.0% better than the state-of-the-art model that requires 3.5B weakly labeled Instagram images. On robustness test sets, it improves ImageNet-A top-1 accuracy from 61.0% to 83.7%, reduces ImageNet-C mean corruption error f... |
B2022-r10-1201|W4200633541 | 2 | collaborative learning | method | Can multiple models improve their robustness through collaborative learning by mutually sharing knowledge of adversarial examples, rather than relying on a static teacher model? | 1,022,485 | 2 | cocited | [] | 0.439 | 0.529 | Densely Connected Convolutional Networks | 2,017 | Recent work has shown that convolutional networks can be substantially deeper, more accurate, and efficient to train if they contain shorter connections between layers close to the input and those close to the output. In this paper, we embrace this observation and introduce the Dense Convolutional Network (DenseNet), w... |
B2022-r10-1201|W4200633541 | 2 | collaborative learning | method | Can multiple models improve their robustness through collaborative learning by mutually sharing knowledge of adversarial examples, rather than relying on a static teacher model? | 218,343 | 2 | cocited | [] | 0.43 | 0.529 | Distilling the Knowledge in a Neural Network | 2,015 | A very simple way to improve the performance of almost any machine learning algorithm is to train many different models on the same data and then to average their predictions. Unfortunately, making predictions using a whole ensemble of models is cumbersome and may be too computationally expensive to allow deployment to... |
B2022-r10-1201|W4200633541 | 3 | mutually sharing knowledge | method | Can multiple models improve their robustness through collaborative learning by mutually sharing knowledge of adversarial examples, rather than relying on a static teacher model? | 1,256,566 | 0 | seed | [
4
] | 0.411 | 0.621 | Towards Deep Learning Models Resistant to Adversarial Attacks | 2,017 | Recent work has demonstrated that deep neural networks are vulnerable to adversarial examples---inputs that are almost indistinguishable from natural data and yet classified incorrectly by the network. In fact, some of the latest findings suggest that the existence of adversarial attacks may be an inherent weakness of ... |
B2022-r10-1201|W4200633541 | 3 | mutually sharing knowledge | method | Can multiple models improve their robustness through collaborative learning by mutually sharing knowledge of adversarial examples, rather than relying on a static teacher model? | 1,779,215 | 0 | seed | [
0
] | 0.392 | 0.621 | Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples | 2,018 | We identify obfuscated gradients, a kind of gradient masking, as a phenomenon that leads to a false sense of security in defenses against adversarial examples. While defenses that cause obfuscated gradients appear to defeat iterative optimization-based attacks, we find defenses relying on this effect can be circumvente... |
B2022-r10-1201|W4200633541 | 3 | mutually sharing knowledge | method | Can multiple models improve their robustness through collaborative learning by mutually sharing knowledge of adversarial examples, rather than relying on a static teacher model? | 2,334,412 | 1 | citer | [
3
] | 0.502 | 0.621 | On the (In)fidelity and Sensitivity for Explanations | 2,019 | We consider objective evaluation measures of saliency explanations for complex black-box machine learning models. We propose simple robust variants of two notions that have been considered in recent literature: (in)fidelity, and sensitivity. We analyze optimal explanations with respect to both these measures, and while... |
B2022-r10-1201|W4200633541 | 3 | mutually sharing knowledge | method | Can multiple models improve their robustness through collaborative learning by mutually sharing knowledge of adversarial examples, rather than relying on a static teacher model? | 2,622,198 | 1 | citer | [
3
] | 0.502 | 0.621 | Understanding Adversarial Examples From the Mutual Influence of Images and Perturbations | 2,020 | A wide variety of works have explored the reason for the existence of adversarial examples, but there is no consensus on the explanation. We propose to treat the DNN logits as a vector for feature representation, and exploit them to analyze the mutual influence of two independent inputs based on the Pearson correlation... |
B2022-r10-1201|W4200633541 | 3 | mutually sharing knowledge | method | Can multiple models improve their robustness through collaborative learning by mutually sharing knowledge of adversarial examples, rather than relying on a static teacher model? | 2,335,542 | 1 | citer | [
3
] | 0.494 | 0.621 | Towards neural networks that provably know when they don't know | 2,019 | It has recently been shown that ReLU networks produce arbitrarily over-confident predictions far away from the training data. Thus, ReLU networks do not know when they don't know. However, this is a highly important property in safety critical applications. In the context of out-of-distribution detection (OOD) there ha... |
B2022-r10-1201|W4200633541 | 3 | mutually sharing knowledge | method | Can multiple models improve their robustness through collaborative learning by mutually sharing knowledge of adversarial examples, rather than relying on a static teacher model? | 218,343 | 2 | cocited | [] | 0.621 | 0.621 | Distilling the Knowledge in a Neural Network | 2,015 | A very simple way to improve the performance of almost any machine learning algorithm is to train many different models on the same data and then to average their predictions. Unfortunately, making predictions using a whole ensemble of models is cumbersome and may be too computationally expensive to allow deployment to... |
B2022-r10-1201|W4200633541 | 3 | mutually sharing knowledge | method | Can multiple models improve their robustness through collaborative learning by mutually sharing knowledge of adversarial examples, rather than relying on a static teacher model? | 1,022,485 | 2 | cocited | [] | 0.471 | 0.621 | Densely Connected Convolutional Networks | 2,017 | Recent work has shown that convolutional networks can be substantially deeper, more accurate, and efficient to train if they contain shorter connections between layers close to the input and those close to the output. In this paper, we embrace this observation and introduce the Dense Convolutional Network (DenseNet), w... |
End of preview.
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
RQB public intermediates (RQ-Rank tracing runs)
These are large, regenerable intermediates from RQ-Rank "trace" runs. Each run directory has two files:
emb.npz: Qwen3-Embedding-4B vectors.UEholds the title and abstract of each neighbourhood paper,urthe corpus row ids, andXEthe RQ elements.verify_jobs.jsonl: the verifier job list, built from those vectors.
Provenance (checked per run directory, 7 Nov 2026).
- Every neighbourhood paper is an OpenAlex work (ids
W…) published before 2026. Titles and abstracts come from OpenAlex, which releases its data under CC0. - Candidate RQs are either LLM extractions from those same OpenAlex papers (ids
W…), or LLM generations (idsL…,B…,A…,C…) from Qwen, Gemma/Gemini, Claude Haiku and similar models. - The set contains no OpenReview reviews, no ICLR 2027 or other under-review submissions, no author-elicited RQs, no expert-judgement data, no clinical data, and no 2026-dated rows.
- Runs excluded for that reason, which stay private:
bao/*(licence unchecked),rqrank_dev/accept_*(external expert-judged sets, 2025–26 rows) andrqrank_demo/*.
Listing with sizes and sha256: MANIFEST.tsv.
Second upload (8 Nov 2026), listed in MANIFEST_2.tsv
Large intermediates that were in no other copy after the private repo filled. Provenance, checked per directory:
rqrank_dev/*,rqrank_test/*: the same runs as above (nodes, elements, verdicts). OpenAlex works (W…, CC0) and LLM-generated or LLM-extracted RQs.ai_vs_human/t7/*: task 7 (SeedTier). Follow-up papers are published OpenAlex/arXiv papers up to 2025. Human questions are LLM extractions from those published papers (title and abstract, or full paper). Model questions are LLM outputs.ai_vs_human/t6x/wd_*_x*,ai_vs_human/t6x/data/nodes_hop.jsonl: model-written questions only, and the published OpenAlex neighbourhood (before the target month) of task-6 papers. This includes public papers dated 2026, which the owner allowed. The task-6 author-written questions and their embeddings are NOT here.task7_followups/*.tar.zst: LLM-extracted RQs from published follow-up papers (recovered/,local_luna/).- Not here: reviews, OpenReview text, ICLR 2027 or other under-review submissions, author-elicited or author-written RQs, expert-judgement data, clinical data, Bao, full-text PDFs, and chat logs.
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